bluuebunny/crossref_metadata_2025 huggingface dataset:
Role of the Prefrontal Cortex in Attentional Control over Bistable Vision
Authors: Windmann Sabine , Wehrmann Michaela , Calabrese Pasquale , Güntürkün Onur
Published: May 2006

The primary source of top-down attentional control in object perception is the prefrontal cortex. This region is involved in the maintenance of goal-related information as well as in attentional selection and set shifting. Recent approaches have emphasized the role of top-down processes during elementary visual processes as exemplified in bistable vision where perception oscillates automatically between two mutually exclusive states. The prefrontal cortex might influence this process either by maintaining the dominant pattern while protecting it against the competing representation, or by facilitating perceptual switches between the two competing representations. To address this issue, we investigated reported perceptual reversals in patients with circumscribed lesions of the prefrontal cortex and healthy control participants in three experimental conditions: hold (maintaining the dominant view), speed (inducing as many perceptual switches as possible), and neutral (no intervention). Results indicated that although the patients showed normal switching rates in the neutral condition and were able to control perceptual switches in the hold condition as much as control subjects were, they were less able to facilitate reversals specifically in the speed condition. These results suggest that the prefrontal cortex is necessary to bias the selection of visual representations in accord with current goals, but is less essential for maintaining selected information active that is continuously available in the environment. As for attentional selection, the present results suggest that the prefrontal cortex initiates perceptual reversals by withdrawing top-down support from the dominant representation without (or prior to) boosting the suppressed view.
Sleep and Wakefulness Out of Phase with Internal Biological Time Impairs Learning in Humans
Authors: Wright Kenneth P. , Hull Joseph T. , Hughes Rod J. , Ronda Joseph M. , Czeisler Charles A.
Published: May 2006

Sleep—wake homeostatic and internal circadian timedependent brain processes interact to regulate human brain function so that alert wakefulness is promoted during the daytime and consolidated sleep is promoted at nighttime. The consequence of chronically altering the normal relationship between these processes for human brain function is largely unknown. We tested cognitive and vigilance performance while subjects lived in the laboratory for over a month. The subjects lived on either 24.0- or 24.6-hr day lengths. Half of the subjects tested maintained a normal relationship between sleep—wakefulness and internal circadian time (synchronized group), whereas the other half did not (nonsynchronized group). Levels of the sleep-promoting hormone melatonin were high during scheduled sleep in the synchronized group, whereas melatonin levels were high during scheduled wakefulness in the nonsynchronized group. Failure to adapt to the scheduled day length was dependent upon individual differences in intrinsic circadian period. Total sleep time was reduced, sleep latency and Rapid Eye Movement (REM) latency were shortened, and wakefulness after sleep onset was increased in the nonsynchronized group. Cognitive performance improved (i.e., learning) in the synchronized group, whereas learning was significantly impaired in the nonsynchronized group. Attention progressively declined in both groups, suggesting that 8 hr of scheduled sleep per night is insufficient to maintain brain vigilance even when sleep occurs at an appropriate biological time. Our results establish that proper alignment between sleep—wakefulness and internal circadian time is crucial for enhancement of cognitive performance. In addition, our results demonstrate that exposure to dim light (~25 lx) is sufficient to expand the range of entrainment in humans.
Attentional Preparation for a Lateralized Visual Distractor: Behavioral and fMRI Evidence
Authors: Ruff Christian C. , Driver Jon
Published: May 2006

Attending to the location of an expected visual target can lead to anticipatory activations in spatiotopic occipital cortex, emerging before target onset. But less is known about how the brain may prepare for a distractor at a known location remote from the target. In a psychophysical experiment, we found that trial-to-trial advance knowledge about the presence of a distractor in the target-opposite hemifield significantly reduced its behavioral cost. In a subsequent functional magnetic resonance imaging experiment with similar task and stimuli, we found anticipatory activations in the occipital cortex contralateral to the expected distractor, but no additional target modulation, when participants were given advance information about a distractor's subsequent presence and location. Several attention-related control structures (frontal eye fields and superior parietal cortex) were active during attentional preparation for all trials, whereas the left superior prefrontal and right angular gyri were additionally activated when a distractor was anticipated. The right temporoparietal junction showed stronger functional coupling with occipital regions during preparation for trials with an isolated target than for trials with a distractor expected. These results show that anticipation of a visual distractor at a known location, remote from the target, can lead to (1) a reduction in the behavioral cost of that distractor, (2) preparatory modulation of the occipital cortex contralateral to the location of the expected distractor, and (3) anticipatory activation of distinct parietal and frontal brain structures. These findings indicate that specific components of preparatory visual attention may be devoted to minimizing the impact of distractors, not just to enhancements of target processing.
Stimulation of the Subthalamic Region Facilitates the Selection and Inhibition of Motor Responses in Parkinson's Disease
Authors: van den Wildenberg Wery P. M. , van Boxtel Geert J. M. , van der Molen Maurits W. , Bosch D. Andries , Speelman Johannes D. , Brunia Cornelis H. M.
Published: May 2006

The aim of the present study was to specify the involvement of the basal ganglia in motor response selection and response inhibition. Two samples were studied. The first sample consisted of patients diagnosed with Parkinson's disease (PD) who received deep-brain stimulation (DBS) of the subthalamic nucleus (STN). The second sample consisted of patients who received DBS for the treatment of PD or essential tremor (ET) in the ventral intermediate nucleus of the thalamus (Vim). Stop-signal task and go/no-go task performances were studied in both groups. Both groups performed these tasks with (on stimulation) and without (off stimulation) DBS to address the question of whether stimulation is effective in improving choice reaction time (RT) and stop-signal RT. The results show that DBS of the STN was associated with significantly enhanced inhibitory control, as indicated by shorter stop-signal RTs. An additional finding is that DBS of the STN led to significantly shorter choice RT. The effects of DBS on responding and response inhibition were functionally independent. Although DBS of the Vim did not systematically affect task performance in patients with ET, a subgroup of Vim-stimulated PD patients showed enhanced stop-signal RTs in on stimulation versus off stimulation. This result suggests that the change in performance to stop signals may not be directly related to STN function, but rather results from a change in PD function due to DBS in general. The findings are discussed in terms of current functional and neurobiological models that relate basal ganglia function to the selection and inhibition of motor responses.
The Role of Intact Frontostriatal Circuits in Error Processing
Authors: Ullsperger Markus , von Cramon D. Yves
Published: May 2006

The basal ganglia have been suggested to play a key role in performance monitoring and resulting behavioral adjustments. It is assumed that the integration of prefrontal and motor cortico—striato—thalamo—cortical circuits provides contextual information to the motor anterior cingulate cortex regions to enable their function in performance monitoring. So far, direct evidence is missing, however. We addressed the involvement of frontostriatal circuits in performance monitoring by collecting event-related brain potentials (ERPs) and behavioral data in nine patients with focal basal ganglia lesions and seven patients with lateral prefrontal cortex lesions while they performed a flanker task. In both patient groups, the amplitude of the error-related negativity was reduced, diminishing the difference to the ERPs on correct responses. Despite these electrophysiological abnormalities, most of the patients were able to correct errors. Only in lateral prefrontal cortex patients whose lesions extended into the frontal white matter, disrupting the connections to the motor anterior cingulate cortex and the striatum, were error corrections severely impaired. In sum, the fronto—striato—thalamo—cortical circuits seem necessary for the generation of error-related negativity, even when brain plasticity has resulted in behavioral compensation of the damage. Thus, error-related ERPs in patients provide a sensitive measure of the integrity of the performance monitoring network.
Explicit versus Implicit Processing of Representational Space in Neglect: Dissociations in Accessing the Mental Number Line
Authors: Priftis Konstantinos , Zorzi Marco , Meneghello Francesca , Marenzi Roberto , Umiltà Carlo
Published: May 2006

The present study investigated the effects of left hemispatial neglect on two tasks activating the mental number line (MNL). Six patients with left neglect performed a mental number bisection task and a modified version of the Spatial Numerical Association of Response Codes (SNARC) task. Effects of left neglect were observed in the number bisection task, but not in the SNARC task. We argue that the dissociation between number bisection and SNARC resembles, in the representational space of the MNL, previously reported dissociations on neglect between explicit knowledge (assessed by direct tasks) and implicit knowledge (assessed by indirect tasks).
Orienting Attention to Points in Time Improves Stimulus Processing Both within and across Modalities
Authors: Lange Kathrin , Röder Brigitte
Published: May 2006

Spatial attention affects the processing of stimuli of both a task-relevant and a task-irrelevant modality. The present study investigated if similar cross-modal effects exist when attention is oriented to a point in time. Short (600 msec) and long (1200 msec) empty intervals, marked by a tactile onset and an auditory or a tactile offset marker, were presented. In each block, the participants had to attend one interval and one modality. Event-related potentials (ERPs) to auditory and tactile offset markers of attended as compared to unattended intervals were characterized by an enhancement of early negative deflections of the auditory and somatosensory ERPs (audition, 100–140 msec; touch, 130–180 msec) when audition or touch was task relevant, respectively. Similar effects were found for auditory stimuli when touch was task relevant. An additional reaction time experiment revealed faster responses to both auditory and tactile stimuli at the attended as compared to the unattended point in time, irrespective of which modality was primary. Both behavioral and ERP data show that attention can be focused on a point in time, which results in a more efficient processing of auditory and tactile stimuli. The ERP data further suggest that a relative enhancement at perceptual processing stages contributes to the processing advantage for temporally attended stimuli. The existence of cross-modal effects of temporal attention underlines the importance of time as a feature for binding input across different modalities.
Neural Activity in Monkey Prefrontal Cortex Is Modulated by Task Context and Behavioral Instruction during Delayed-match-to-sample and Conditional Prosaccade—Antisaccade Tasks
Authors: Johnston Kevin , Everling Stefan
Published: May 2006

Complex behavior often requires the formation of associations between environmental stimuli and motor responses appropriate to those stimuli. Moreover, the appropriate response to a given stimulus may vary depending on environmental context. Stimulus-response associations that are adaptive in one situation may not be in another. The prefrontal cortex (PFC) has been shown to be critical for stimulus-response mapping and the implementation of task context. To investigate the neural representation of sensory-motor associations and task context in the PFC, we recorded the activity of prefrontal neurons in two monkeys while they performed two tasks. The first task was a delayed-match-to-sample task in which monkeys were presented with a sample picture and rewarded for making a saccade to the test picture that matched the sample picture following a delay period. The second task was a conditional visuomotor task in which identical sample pictures were presented. In this task, animals were rewarded for performing either prosaccades or antisaccades following the delay period depending on sample picture identity. PFC neurons showed task selectivity, object selectivity, and combinations of task and object selectivity. These modulations of activity took the form of a reduction in stimulus and delay-related activity, and a pro/anti instruction-based grouping of delay activity in the conditional visuomotor task. These data show that activity in PFC neurons is modulated by experimental context, and that this activity represents the formal demands of the task currently being performed.
Quintet.net: An Environment for Composing and Performing Music on the Internet
Authors: Hajdu Georg
Published: May 2006

Quintet.net is a real-time interactive environment for intermedial composition and performance on local networks as well as the Internet. Since its premiere in 2000, the environment has been used in several large projects connecting players in Europe and the U.S.A., a Munich biennale opera project among them. Quintet.net implements, in a virtual environment, the metaphor of five performers under the control of a conductor, thus dealing with important aspects of symbolic, aural and visual communication among the participants and the network audience. A composition development kit has been added to the environment (which consists of Client, Server, Listener, Conductor and Viewer) to facilitate the development of pieces that take full advantage of the wide continuum between composition and improvisation.
Artists in Industry and the Academy: Collaborative Research, Interdisciplinary Scholarship and the Creation and Interpretation of Hybrid Forms
Authors: Shanken Edward A.
Published: May 2006

The author surveys contemporary artist-engineer-scientist collaborations in industry and the academy and considers a variety of theoretical and practical issues pertaining to them. Given the increasing dedication of cultural resources to engage artists and designers in science and technology research, the author concludes that more scholarship must analyze case studies, identify best practices and working methods, and propose models for evaluating both the hybrid products resulting from these endeavors and the contributions of the individuals engaged in them.
Innovation and Visualization: Trajectories, Strategies and Myths
Authors: Ione Amy
Published: May 2006

The extent to which thinking about art has been shaped by the predominantly descriptive approach of the past is a topic in need of review. This paper summarizes historical influences that stimulated the textual turn and traces the evolution toward more visual communication.
Digitizing the Golem: From Earth to Outer Space
Authors: Rapoport Sonya
Published: May 2006

Sonya Rapoport, a multimedia artist, traces five decades of her career, coalescing art, science and technology. She provides a personal history in a scientific context that leads her from painting and abstract expressionism to computer-assisted interactive installations and webworks. The artist uses the metaphor of the golem, her self-described avatar, as a means to question and explain the autobiographical evolution of her iconography, choice of media and style. Her journey includes the influences on her interdisciplinary art expression in which anthropology, chemistry, botany, religion and topics of gender and transculture are intermittently treated with parody, humor and mysticism.
Proteins, Immersive Games and Music
Authors: Cai Y.Y. , Lu B.F. , Fan Z.W. , Chan C.W. , Lim K.T. , Qi L. , Li L.
Published: May 2006

The authors present their research on using virtual reality (VR) in the presentation of protein music and immersive games. They first describe the core components of VR technology for protein modeling, visualization and interaction. They then present their implementations of VR protein games and protein-derived computer music. Instruction in protein-structure learning is discussed in the context of the authors' trial project in the Chinese High School in Singapore and an exhibition at Singapore Art Museum.
Cognitive and Evolutionary Speculations for Biomorphic Architecture
Authors: Joye Yannick
Published: May 2006

Evolution in natural environments has endowed humans with a cognitive system that is specialized in processing information about living things. Yet the presence of living things in the human living environment is ever decreasing, which leads to the underdevelopment of this system. The author speculates that biomorphic architecture or design can counteract this trend, which would have positive implications for various aspects of human functioning and could provoke subtle shifts in certain areas of human thinking.
Moments of the Maiden: Time, Space and Intercultural Communication in Tokyo
Authors: Jaimes Alejandro
Published: May 2006

A dispatch on the arts, technologies and cultures in the metropolitan community served by the Narita airport.
Extended Memory: Early Calculating Engines and Historical Computer Simulations
Authors: Mather David
Published: May 2006

When framed within cognitive theory's extended mind hypothesis, Charles Babbage's 19th-century calculating machines illustrate a distinction between accuracy and flexibility. These properties affect how historical data and memory are organized, providing conceptual linkages for mind-machine integration. The distinction between accuracy and flexibility is also apparent in present-day computer simulations that use historical scenarios, such as virtual-reality software designed for the Bloody Sunday Inquiry, history-based video games and other art and entertainment software applications. These contemporary examples share one important feature of extended mind: the incorporation of history or personal memory into a shared memory system.
Reduction of Physiological Stress Using Fractal Art and Architecture
Authors: Taylor R.P.
Published: May 2006

The author reviews visual perception studies showing that fractal patterns possess an aesthetic quality based on their visual complexity. Specifically, people display an aesthetic preference for patterns with mid-range fractal dimensions, irrespective of the method used to generate them. The author builds upon these studies by presenting preliminary research indicating that mid-range fractals also affect the observer's physiological condition. The potential for incorporating these fractals into art and architecture as a novel approach to reducing stress is also discussed.
A Simple Proof That Optimality Theory Is Computationally Intractable
Authors: Idsardi William J.
Published: May 2006

Adapting arguments from Eisner 1997, 2000, this remark provides a simple proof that the generation problem for Optimality Theory (Prince and Smolensky 2004) is NP-hard. The proof needs only the binary evaluation of constraints and uses only constraints generally employed in the Optimality Theory literature. In contrast, rule-based derivational systems are easily computable, belonging to the class of polynomialtime algorithms, P (Eisner 2000).
In Defense of a Quantificational Account of Definite DPs
Authors: Isac Daniela
Published: May 2006

Two types of arguments support a quantificational view of definite DPs. First, definite DPs share properties with other quantified expressions. In particular, they pattern together in antecedent-contained deletion constructions, they show weak crossover effects, and at least some of them interact scopally with other quantified expressions. Second, the apparent failure of (some) definite DPs to interact scopally with other quantified expressions and to exhibit island effects stems from two properties of definite DPs: they are all principal filters, and the witness set of singular definite DPs is a singleton. These two properties have the effect of rendering the wide and narrow scope readings of definite DPs indistinguishable.
Flat Minima
Authors: Hochreiter Sepp , Schmidhuber Jürgen
Published: May 2006

We present a new algorithm for finding low-complexity neural networks with high generalization capability. The algorithm searches for a “flat” minimum of the error function. A flat minimum is a large connected region in weight space where the error remains approximately constant. An MDL-based, Bayesian argument suggests that flat minima correspond to “simple” networks and low expected overfitting. The argument is based on a Gibbs algorithm variant and a novel way of splitting generalization error into underfitting and overfitting error. Unlike many previous approaches, ours does not require gaussian assumptions and does not depend on a “good” weight prior. Instead we have a prior over input output functions, thus taking into account net architecture and training set. Although our algorithm requires the computation of second-order derivatives, it has backpropagation's order of complexity. Automatically, it effectively prunes units, weights, and input lines. Various experiments with feedforward and recurrent nets are described. In an application to stock market prediction, flat minimum search outperforms conventional backprop, weight decay, and “optimal brain surgeon/optimal brain damage.”
Neural Networks for Functional Approximation and System Identification
Authors: Mhaskar H. N. , Hahm Nahmwoo
Published: May 2006

We construct generalized translation networks to approximate uniformly a class of nonlinear, continuous functionals defined on Lp([—1, 1]s) for integer s ≥ 1, 1 ≤ p < ∞, or C([—1, 1]s). We obtain lower bounds on the possible order of approximation for such functionals in terms of any approximation process depending continuously on a given number of parameters. Our networks almost achieve this order of approximation in terms of the number of parameters (neurons) involved in the network. The training is simple and noniterative; in particular, we avoid any optimization such as that involved in the usual backpropagation.
A Penalty-Function Approach for Pruning Feedforward Neural Networks
Authors: Setiono Rudy
Published: May 2006

This article proposes the use of a penalty function for pruning feedforward neural network by weight elimination. The penalty function proposed consists of two terms. The first term is to discourage the use of unnecessary connections, and the second term is to prevent the weights of the connections from taking excessively large values. Simple criteria for eliminating weights from the network are also given. The effectiveness of this penalty function is tested on three well-known problems: the contiguity problem, the parity problems, and the monks problems. The resulting pruned networks obtained for many of these problems have fewer connections than previously reported in the literature.
Extracting Rules from Neural Networks by Pruning and Hidden-Unit Splitting
Authors: Setiono Rudy
Published: May 2006

An algorithm for extracting rules from a standard three-layer feedforward neural network is proposed. The trained network is first pruned not only to remove redundant connections in the network but, more important, to detect the relevant inputs. The algorithm generates rules from the pruned network by considering only a small number of activation values at the hidden units. If the number of inputs connected to a hidden unit is sufficiently small, then rules that describe how each of its activation values is obtained can be readily generated. Otherwise the hidden unit will be split and treated as output units, with each output unit corresponding to an activation value. A hidden layer is inserted and a new subnetwork is formed, trained, and pruned. This process is repeated until every hidden unit in the network has a relatively small number of input units connected to it. Examples on how the proposed algorithm works are shown using real-world data arising from molecular biology and signal processing. Our results show that for these complex problems, the algorithm can extract reasonably compact rule sets that have high predictive accuracy rates.
Detecting Synchronous Cell Assemblies with Limited Data and Overlapping Assemblies
Authors: Strangman Gary
Published: May 2006

Two statistical methods—cross-correlation (Moore et al. 1966) and gravity clustering (Gerstein et al. 1985)—were evaluated for their ability to detect synchronous cell assemblies from simulated spike train data. The two methods were first analyzed for their temporal sensitivity to synchronous cell assemblies. The presented approach places a lower bound on the amount of data required to detect a synchronous assembly. On average, both methods required the same minimum amount of recording time to detect significant pairwise correlations, but the gravity method exhibited less variance in the recording time. The precise length of recording depends on the consistency with which a neuron fires synchronously with the assembly but was independent of the assembly firing rate. Next, the statistical methods were tested with respect to their ability to differentiate two distinct assemblies that overlapped in time and space. Both statistics could adequately differentiate two overlapping synchronous assemblies. For cross-correlation, this ability deteriorates quickly when considering three or more simultaneously active, overlapping assemblies, whereas the gravity method should be more flexible in this regard. The work demonstrates the difficulty of detecting assembly phenomena from simultaneous neuronal recordings. Other statistical methods and the detection of other types of assemblies are also discussed.
Playing Billiards in Version Space
Authors: Ruján Pál
Published: May 2006

A ray-tracing method inspired by ergodic billiards is used to estimate the theoretically best decision rule for a given set of linear separable examples. For randomly distributed examples, the billiard estimate of the single Perceptron with best average generalization probability agrees with known analytic results, while for real-life classification problems, the generalization probability is consistently enhanced when compared to the maximal stability Perceptron.
Effect of Delay on the Boundary of the Basin of Attraction in a Self-Excited Single Graded-Response Neuron
Authors: Pakdaman K. , Malta C. P. , Grotta-Ragazzo C. , Vibert J.-F.
Published: May 2006

Little attention has been paid in the past to the effects of interunit transmission delays (representing a xonal and synaptic delays) ontheboundary of the basin of attraction of stable equilibrium points in neural networks. As a first step toward a better understanding of the influence of delay, we study the dynamics of a single graded-response neuron with a delayed excitatory self-connection. The behavior of this system is representative of that of a family of networks composed of graded-response neurons in which most trajectories converge to stable equilibrium points for any delay value. It is shown that changing the delay modifies the “location” of the boundary of the basin of attraction of the stable equilibrium points without affecting the stability of the equilibria. The dynamics of trajectories on the boundary are also delay dependent and influence the transient regime of trajectories within the adjacent basins. Our results suggest that when dealing with networks with delay, it is important to study not only the effect of the delay on the asymptotic convergence of the system but also on the boundary of the basins of attraction of the equilibria.
An Analog Memory Circuit for Spiking Silicon Neurons
Authors: Elias John G. , Northmore David P. M. , Westerman Wayne
Published: May 2006

A simple circuit is described that functions as an analog memory whose state and dynamics are directly controlled by pulsatile inputs. The circuit has been incorporated into a silicon neuron with a spatially extensive dendritic tree as a means of controlling the spike firing threshold of an integrate-and-fire soma. Spiking activity generated by the neuron itself and by other units in a network can thereby regulate the neuron's excitability over time periods ranging from milliseconds to many minutes. Experimental results are presented showing applications to temporal edge sharpening, bistable behavior, and a network that learns in the manner of classical conditioning.
Conductance-Based Integrate-and-Fire Models
Authors: Destexhe Alain
Published: May 2006

A conductance-based model of Na+ and K+ currents underlying action potential generation is introduced by simplifying the quantitative model of Hodgkin and Huxley (HH). If the time course of rate constants can be approximated by a pulse, HH equations can be solved analytically. Pulse-based (PB) models generate action potentials very similar to the HH model but are computationally faster. Unlike the classical integrate-and fire (IAF) approach, they take into account the changes of conductances during and after the spike, which have a determinant influence in shaping neuronal responses. Similarities and differences among PB, IAF, and HH models are illustrated for three cases: high-frequency repetitive firing, spike timing following random synaptic inputs, and network behavior in the presence of intrinsic currents.
Functional Periodic Intracortical Couplings Induced by Structured Lateral Inhibition in a Linear Cortical Network
Authors: Sabatini S. P. , Bisio G. M. , Raffo L.
Published: May 2006

The spatial organization of cortical axon and dendritic fields could be an interesting structural paradigm to obtain a functional specificity with out postulating highly specific feedforward connections. In this article, we investigate the functional implications of recurrent intracortical inhibition when it occurs through clustered medium-range interconnection schemes (Wörgötter & Koch, 1991; Somogyi, 1989; Kritzer, Cowey, & Somogyi, 1992). Moreover, the interaction between the inhibitory schemes and visual orientation maps is explored. Assuming linearity, we show that clustered inhibitory mechanisms can trigger a propagation process that allows the development of extra (i.e., induced) interactions among the cortical sites involved in the recurrent loops. In addition, we point out how these interactions functionally modify the response of cortical simple cells and yield to highly structured Gabor-like receptive fields. This study should be considered not as a realistic biological model of the primary visual cortex but as an attempt to explain possible computational principles related to intracortical connectivity and to the underlying single-cell properties.
Singularities in Primate Orientation Maps
Authors: Obermayer K. , Blasdel G. G.
Published: May 2006

We report the results of an analysis of orientation maps in primate striate cortex with focus on singularities and their distribution. Data were obtained from squirrel monkeys and macaque monkeys of different ages. We find that approximately 80% of singularities that are nearest neighbors have the opposite sign and that the spatial distribution of singularities differs significantly from a random distribution of points. We do not find evidence for consistent geometric patterns that singularities may form across the cortex. Except for a different overall alignment of orientation bands and different periods of repetition, maps obtained from different animals and different ages are found similar with respect to the measures used.

Orientation maps are then compared with two different pattern models that are currently discussed in the literature: bandpass-filtered white noise, which accounts very well for the overall map structure, and the field analogy model, which specifies the orientation map by the location of singularities and their properties. The bandpass-filtered noise approach to orientation patterns correctly predicts the sign correlations between singularities and accounts for the deviations in the spatial distribution of singularities away from a random dot pattern. The field analogy model can account for the structure of certain local patches of the orientation map but not for the whole map. Neither of the models is completely satisfactory, and the structure of the orientation map remains to be fully explained.
Topographic Receptive Fields and Patterned Lateral Interaction in a Self-Organizing Model of the Primary Visual Cortex
Authors: Sirosh Joseph , Miikkulainen Risto
Published: May 2006

This article presents a self-organizing neural network model for the simultaneous and cooperative development of topographic receptive fields and lateral interactions in cortical maps. Both afferent and lateral connections adapt by the same Hebbian mechanism in a purely local and unsupervised learning process. Afferent input weights of each neuron self organize into hill-shaped profiles, receptive fields organize topographically across the network, and unique lateral interaction profiles develop for each neuron. The model demonstrates how patterned lateral connections develop based on correlated activity and explains why lateral connection patterns closely follow receptive field properties such as ocular dominance.
The Formation of Topographic Maps That Maximize the Average Mutual Information of the Output Responses to Noiseless Input Signals
Authors: Hulle Marc M. Van
Published: May 2006

This article introduces an extremely simple and local learning rule for to pographic map formation. The rule, called the maximum entropy learning rule (MER), maximizes the unconditional entropy of the map's output for any type of input distribution. The aim of this article is to show that MER is a viable strategy for building topographic maps that maximize the average mutual information of the output responses to noiseless input signals when only input noise and noise-added input signals are available.
Hyperparameter Selection for Self-Organizing Maps
Authors: Utsugi Akio
Published: May 2006

The self-organizing map (SOM) algorithm for finite data is derived as an approximate maximum a posteriori estimation algorithm for a gaussian mixture model with a gaussian smoothing prior, which is equivalent to a generalized deformable model (GDM). For this model, objective criteria for selecting hyperparameters are obtained on the basis of empirical Bayesian estimation and cross-validation, which are representative model selection methods. The properties of these criteria are compared by simulation experiments. These experiments show that the cross-validation methods favor more complex structures than the expected log likelihood supports, which is a measure of compatibility between a model and data distribution. On the other hand, the empirical Bayesian methods have the opposite bias.
Covariance Learning of Correlated Patterns in Competitive Networks
Authors: Minai Ali A.
Published: May 2006

Covariance learning is a powerful type of Hebbian learning, allowing both potentiation and depression of synaptic strength. It is used for associative memory in feedforward and recurrent neural network paradigms. This article describes a variant of covariance learning that works particularly well for correlated stimuli in feedforward networks with competitive K-of-N firing. The rule, which is nonlinear, has an intuitive mathematical interpretation, and simulations presented in this article demonstrate its utility.
Similarity, Connectionism, and the Problem of Representation in Vision
Authors: Edelman Shimon , Duvdevani-Bar Sharon
Published: May 2006

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. This article discusses the computational requirements of representation that preserves similarity ranks and points out the relative straightforwardness of its connectionist implementation.
Dynamic Model of Visual Recognition Predicts Neural Response Properties in the Visual Cortex
Authors: Rao Rajesh P. N. , Ballard Dana H.
Published: May 2006

The responses of visual cortical neurons during fixation tasks can be significantly modulated by stimuli from beyond the classical receptive field. Modulatory effects in neural responses have also been recently reported in a task where a monkey freely views a natural scene. In this article, we describe a hierarchical network model of visual recognition that explains these experimental observations by using a form of the extended Kalman filter as given by the minimum description length (MDL) principle. The model dynamically combines input-driven bottom-up signals with expectation-driven top-down signals to predict current recognition state. Synaptic weights in the model are adapted in a Hebbian manner according to a learning rule also derived from the MDL principle. The resulting prediction-learning scheme can be viewed as implementing a form of the expectation-maximization (EM) algorithm. The architecture of the model posits an active computational role for the reciprocal connections between adjoining visual cortical areas in determining neural response properties. In particular, the model demonstrates the possible role of feedback from higher cortical areas in mediating neurophysiological effects due to stimuli from beyond the classical receptive field. Simulations of the model are provided that help explain the experimental observations regarding neural responses in both free viewing and fixating conditions.
Correction to “Lower Bounds on VC-Dimension of Smoothly Parameterized Function Classes”
Authors: Lee Wee Sun , Bartlett Peter L. , Williamson Robert C.
Published: May 2006

The earlier article gives lower bounds on the VC-dimension of various smoothly parameterized function classes. The results were proved by showing a relationship between the uniqueness of decision boundaries and the VC-dimension of smoothly parameterized function classes. The proof is incorrect; there is no such relationship under the conditions stated in the article. For the case of neural networks with tanh activation functions, we give an alternative proof of a lower bound for the VC-dimension proportional to the number of parameters, which holds even when the magnitude of the parameters is restricted to be arbitrarily small.
Marr's Theory of the Neocortex as a Self-Organizing Neural Network
Authors: Willshaw David , Hallam John , Gingell Sarah , Lau Soo Leng
Published: May 2006

Marr's proposal for the functioning of the neocortex (Marr, 1970) is the least known of his various theories for specific neural circuitries. He suggested that the neocortex learns by self-organization to extract the structure from the patterns of activity incident upon it. He proposed a feedforward neural network in which the connections to the output cells (identified with the pyramidal cells of the neocortex) are modified by a mechanism of competitive learning. It was intended that each output cell comes to be selective for the input patterns from a different class and is able to respond to new patterns from the same class that have not been seen before. The learning rule that Marr proposed was underspecified, but a logical extension of the basic idea results in a synaptic learning rule in which the total amount of synaptic strength of the connections from each input (“presynaptic”) cell is kept at a constant level. In contrast, conventional competitive learning involves rules of the “postsynaptic” type. The network learns by exploiting the structure that Marr assumed to exist within the ensemble of input patterns. For this case, analysis is possible that extends that carried out by Marr, which was restricted to the binary classification task. This analysis is presented here, together with results from computer simulations of different types of competitive learning mechanisms. The presynaptic mechanism is best known in the computational neuroscience literature. In neural network applications, it may be a more suitable mechanism of competitive learning than those normally considered.
Noise Adaptation in Integrate-and-Fire Neurons
Authors: Rudd Michael E. , Brown Lawrence G.
Published: May 2006

The statistical spiking response of an ensemble of identically prepared stochastic integrate-and-fire neurons to a rectangular input current plus gaussian white noise is analyzed. It is shown that, on average, integrate-and-fire neurons adapt to the root-mean-square noise level of their input. This phenomenon is referred to as noise adaptation. Noise adaptation is characterized by a decrease in the average neural firing rate and an accompanying decrease in the average value of the generator potential, both of which can be attributed to noise-induced resets of the generator potential mediated by the integrate-and-fire mechanism. A quantitative theory of noise adaptation in stochastic integrate-and-fire neurons is developed. It is shown that integrate-and-fire neurons, on average, produce transient spiking activity whenever there is an increase in the level of their input noise. This transient noise response is either reduced or eliminated over time, depending on the parameters of the model neuron. Analytical methods are used to prove that nonleaky integrate-and-fire neurons totally adapt to any constant input noise level, in the sense that their asymptotic spiking rates are independent of the magnitude of their input noise. For leaky integrate-and-fire neurons, the long-run noise adaptation is not total, but the response to noise is partially eliminated. Expressions for the probability density function of the generator potential and the first two moments of the potential distribution are derived for the particular case of a nonleaky neuron driven by gaussian white noise of mean zero and constant variance. The functional significance of noise adaptation for the performance of networks comprising integrate-and-fire neurons is discussed.
Noise Injection: Theoretical Prospects
Authors: Grandvalet Yves , Canu Stéphane , Boucheron Stéphane
Published: May 2006

Noise injection consists of adding noise to the inputs during neural network training. Experimental results suggest that it might improve the generalization ability of the resulting neural network. A justification of this improvement remains elusive: describing analytically the average perturbed cost function is difficult, and controlling the fluctuations of the random perturbed cost function is hard. Hence, recent papers suggest replacing the random perturbed cost by a (deterministic) Taylor approximation of the average perturbed cost function. This article takes a different stance: when the injected noise is gaussian, noise injection is naturally connected to the action of the heat kernel. This provides indications on the relevance domain of traditional Taylor expansions and shows the dependence of the quality of Taylor approximations on global smoothness properties of neural networks under consideration. The connection between noise injection and heat kernel also enables controlling the fluctuations of the random perturbed cost function. Under the global smoothness assumption, tools from gaussian analysis provide bounds on the tail behavior of the perturbed cost. This finally suggests that mixing input perturbation with smoothness-based penalization might be profitable.
Analysis of Dynamical Recognizers
Authors: Blair Alan D. , Pollack Jordan B.
Published: May 2006

Pollack (1991) demonstrated that second-order recurrent neural networks can act as dynamical recognizers for formal languages when trained on positive and negative examples, and observed both phase transitions in learning and interacted function system-like fractal state sets. Follow on work focused mainly on the extraction and minimization of a finite state automaton (FSA) from the trained network. However, such networks are capable of inducing languages that are not regular and therefore not equivalent to any FSA. Indeed, it may be simpler for a small network to fit its training data by inducing such a nonregular language. But when is the network's language not regular? In this article, using a low-dimensional network capable of learning all the Tomita data sets, we present an empirical method for testing whether the language induced by the network is regular. We also provide a detailed "-machine analysis of trained networks for both regular and nonregular languages.
A Bound on the Error of Cross Validation Using the Approximation and Estimation Rates, with Consequences for the Training-Test Split
Authors: Kearns Michael
Published: May 2006

We give a theoretical and experimental analysis of the generalization error of cross validation using two natural measures of the problem under consideration. The approximation rate measures the accuracy to which the target function can be ideally approximated as a function of the number of parameters, and thus captures the complexity of the target function with respect to the hypothesis model. The estimation rate measures the deviation between the training and generalization errors as a function of the number of parameters, and thus captures the extent to which the hypothesis model suffers from overfitting. Using these two measures, we give a rigorous and general bound on the error of the simplest form of cross validation. The bound clearly shows the dangers of making γ —the fraction of data saved for testing—too large or too small. By optimizing the bound with respect to γ, we then argue that the following qualitative properties of cross-validation behavior should be quite robust to significant changes in the underlying model selection problem: When the target function complexity is small compared to the sample size, the performance of cross validation is relatively insensitive to the choice of γ. The importance of choosing γ optimally increases, and the optimal value for γ decreases, as the target function becomes more complex relative to the sample size. There is nevertheless a single fixed value for γ that works nearly optimally for a wide range of target function complexity.
Averaging Regularized Estimators
Authors: Taniguchi Michiaki , Tresp Volker
Published: May 2006

We compare the performance of averaged regularized estimators. We show that the improvement in performance that can be achieved by averaging depends critically on the degree of regularization which is used in training the individual estimators. We compare four different averaging approaches: simple averaging, bagging, variance-based weighting, and variance-based bagging. In any of the averaging methods, the greatest degree of improvement—if compared to the individual estimators—is achieved if no or only a small degree of regularization is used. Here, variance-based weighting and variance-based bagging are superior to simple averaging or bagging. Our experiments indicate that better performance for both individual estimators and for averaging is achieved in combination with regularization. With increasing degrees of regularization, the two bagging-based approaches (bagging and variance-based bagging) outperform the individual estimators, simple averaging, and variance-based weighting. Bagging and variance-based bagging seem to be the overall best combining methods over a wide range of degrees of regularization.