bluuebunny/crossref_metadata_2025 huggingface dataset:
Tumor-Specific T-Cell Memory: Clearing the Regulatory T-Cell Hurdle
Authors: Côté Anik L. , Usherwood Edward J. , Turk Mary Jo
Published: March 2008

Antitumor immune responses can be stimulated by interfering with regulatory T-cell (Treg) function. However, this effect is short lived unless T-cell memory to tumor antigens can be generated. Our recent studies show that Treg cells not only limit primary responses to tumor/self-antigens in tumor-bearing hosts but also prevent the natural generation of T-cell memory to such antigens. Here, we discuss the role of Treg cells in suppressing T-cell memory after surgical excision of tumors and the potential clinical benefits of overcoming this suppression. [Cancer Res 2008;68(6):1614–7]
Advances in the Management of Paediatric Cushing’s Disease
Authors: Savage Martin O. , Chan Li F. , Afshar Farhad , Plowman P. Nicholas , Grossman Ashley B. , Storr Helen L.
Published: March 2008

Cushing’s disease (CD) is rare in the paediatric age range, but may present a difficult therapeutic challenge. Most paediatric endocrinologists have limited experience managing children or adolescents with CD and thus benefit from close consultation with adult colleagues. Prior to definitive treatment, a diagnostic protocol for investigation is required which broadly follows the model for adult patients. Treatment strategies for CD are described and critically appraised. The management of paediatric CD patients after cure also presents challenges for optimizing growth, bone health, reproduction and body composition from childhood into and during adult life.
A Very Low Dose Intravenous Dexamethasone Suppression Test as an Index of Glucocorticoid Sensitivity
Authors: Faria Cláudia Dutra Costantin , Cobra Jayme F. , Sousa e Silva Tatiane , Melo Murilo Rezende , Rocha Mylene Neves , Hayashi Lílian Sukusima , Faria Thelma Gomes , de Souza e Almeida Julio Antonio , Kater Cláudio Elias , Longui Carlos Alberto
Published: March 2008

Background/Aims: The wide variability of responses to corticotherapy suggests a role for individual recognition of steroid sensitivity in order to customize treatment. Oral dexamethasone (DEX) administration may be hindered by the rate of its intestinal absorption and the liver first-passage effect. In this study we suggest that an intravenous very low dose DEX suppression test (VLD IV-DST) can be used as an index for glucocorticoid (GC) sensitivity. Methods: We evaluated 87 normal subjects: 44 prepubertal children, 23 adolescents and 20 adults with a VLD IV-DST using 20 µg/m2 of DEX (dose able to recognize GC sensitivity). Cortisol was initially measured at several time-points after DEX prompting us to establish its nadir and subsequent simplification of the test by measuring cortisol at baseline and after 120 min. Results: Baseline cortisol was similar in adolescents and in adults, but lower in children. There was a spectrum of individual responses in all age groups. The percent reduction of cortisol after 120 min was different in these three age groups, with median values of 44.4% in children, 25.9% in adolescents and 61.6% in adults. Conclusion: This simplified VLD IV-DST using 20 µg/m2 of DEX is useful to evaluate individual sensitivity to GC in different age groups.
A Case of Takotsubo Cardiomyopathy after Surgery for Common Bile Duct Stones
Authors: Tori Masayuki , Ueshima Shigeyuki , Nakahara Masaaki
Published: March 2008

Takotsubo cardiomyopathy (TC) is a rare reversible circulatory syndrome often detected in elderly patients after general surgery. We report the case of an 85-year-old woman who was admitted for obstructive jaundice and underwent ERCP and ENBD before operation. She finally underwent cholecystectomy and choledocholithotomy with the diagnosis of gallbladder and common bile duct stones. In the evening just after the operation, without symptoms of cardiac failure, her ECG suddenly exhibited abnormal ST-T elevation in leads II, III, and V2–V6, though neither CPK(MB) nor troponin T was elevated. Echocardiogram demonstrated basal hyperkinesis and apical dyskinesis, suggesting TC. Conservative treatment enabled recovery, ECG was normalized in a month, and the postoperative course was satisfactory. The stress on her during treatment might have been a cause of this unique cardiomyopathy, and it should be recalled that TC is a potential complication of hepato-biliary-pancreatic surgery.
Primary Hepatosplenic Large B-Cell Lymphoma
Authors: Morales-Polanco M.R. , Drijansky-Morgenstern R. , Murillo-Meza E. , Gómez-Morales E.
Published: March 2008

Diffuse large B-cell lymphoma is the most common form of lymphoma. It usually begins in the lymph nodes; up to 40% may have an extranodal presentation. According to a definition of primary extranodal lymphoma with presentation only in extranodal sites, there are reports of large B-cell lymphomas limited to liver or spleen as separate entities, and to date there have been only three documented cases of primary hepatosplenic presentation. This paper reports a fourth case. Due to a review of the literature and the clinical course of the case reported, we conclude that primary hepatosplenic large B-cell lymphoma has been found predominantly in females older than 60 years. The patients reported had <2 months of evolution prior to diagnosis, prominent B symptoms, splenomegaly in three and hepatomegaly in two, none with lymph node involvement. All had thrombocytopenia and abnormal liver function tests; three had anemia and elevated serum lactic dehydrogenase levels, two with hemophagocytosis in bone marrow. Because of the previously mentioned data, it can be stated that primary hepatosplenic lymphoma is an uncommon and aggressive form of disease that requires immediate recognition and treatment.
Analysis of UBQLN1 Variants in a Polish Alzheimer’s Disease Patient: Control Series
Authors: Golan Maciej P. , Melquist Stacey , Safranow Krzysztof , Styczyńska Maria , Słowik Agnieszka , Kobryś Małgorzata , Żekanowski Cezary , Barcikowska Maria
Published: March 2008

Late-onset Alzheimer’s disease (LOAD) is the most common neurodegenerative disorder, and has a complex etiology. Recently an intronic polymorphism in the ubiquilin 1 gene (UBQLN1) and a particular haplotype was reported to be associated with LOAD. We investigated whether variants in UBQLN1 confer a risk for the disease in 407 Polish LOAD patients and 407 controls. We observed a weak association with the rs2781002 polymorphism, however, contrary to the initial reports, in our group the association was with the A allele. Risk estimation for AA versus GG genotypes showed that the AA genotype is a weak risk factor for AD (OR = 1.8, 95% CI = 1.1–3.1, p = 0.025). This effect was stronger in a group of LOAD patients without APOE4 allele. Haplotype analyses indicate that there is an increase of haplotypes with an A allele in the case group. Also, the specific haplotypes with the A allele that increase AD risk differ between the APOE4-positive and APOE4-negative pools. However, the association observed seems to be driven mostly by rare (<5%) haplotypes. Results suggest a need for additional association studies and in silico analysis of the UBQLN1 locus.
Major Neurologic Improvement following Endovascular Recanalization Therapy for Acute Ischemic Stroke
Authors: Prabhakaran Shyam , Chen Michael , Choi Jae H. , Mangla Sundeep , Lavine Sean D. , Pile-Spellman John , Meyers Philip M. , Chong Ji Y
Published: March 2008

Background: We aimed to identify the rate of major neurologic improvement (MNI) at 24 h following endovascular recanalization therapy (ERT) for acute ischemic stroke and its association with short-term outcome. Methods: We retrospectively reviewed consecutive acute ischemic stroke patients presenting to our institution over 4 years and undergoing ERT. Angiograms were independently reviewed. Data on demographics, medical history, initial NIHSS score, 24-hour NIHSS score, site of acute vascular lesion, pre- and posttreatment Thrombolysis in Myocardial Infarction scores, symptomatic intracerebral hemorrhage (within 36 h of intervention that was associated with a 4-point decline in NIHSS score) and discharge disposition were collected. We used logistic regression analysis to identify predictors of MNI (defined as ≥8-point improvement in NIHSS or a score of 0–1 at 24 h) and favorable discharge status (defined as home or acute rehabilitation). Results: Sixty-eight patients were included (median age = 71 years, 60% women, median NIHSS score = 19.5, anterior circulation = 75%). The modes of ERT were pharmacologic only (28%), mechanical only (35%) and multimodal therapy (37%). Thrombolysis in Myocardial Infarction 2 or 3 recanalization was achieved in 64.7% (mechanical only 46%, pharmacologic only 63% and multimodal 84%). The outcomes were: symptomatic intracerebral hemorrhage (11.8%), MNI (26.5%) and favorable discharge (41.2%). Age (OR = 0.93, p = 0.003) and cardioembolic stroke subtype (OR = 6.0, p = 0.018) were independent predictors of MNI. MNI was a strong predictor of favorable discharge status (OR = 46.4, p < 0.001). Conclusions: Despite initial stroke severity, MNI occurred in over one fourth of the patients and independently and strongly predicted favorable discharge outcome.
Diagnosing Cerebral Collateral Flow Patterns: Accuracy of Non-Invasive Testing
Authors: Hendrikse Jeroen , Klijn Catharina J.M. , van Huffelen Alexander C. , Kappelle L. Jaap , van der Grond Jeroen
Published: March 2008

Objective: With routine use of non-invasive methods to assess the internal carotid artery, it becomes increasingly important to clarify the diagnostic accuracy of transcranial Doppler (TCD) ultrasound and magnetic resonance angiography (MRA) compared with intra-arterial digital subtraction angiography (iaDSA) for the detection of collateral flow via the major intracerebral collateral branches. Subjects and Methods: In a prospective study, we compared TCD via a temporal bone window and MRA (flow direction sensitive phase contrast and time of flight) examinations of the intracranial collateral flow with iaDSA in a cohort of 97 consecutive patients with recent transient or minor disabling cerebral ischaemia associated with an occlusion of the carotid artery (38 contralateral stenosis >70%). Results: iaDSA allowed the evaluation of collateral flow via the anterior and posterior circle of Willis in 97 and 67 patients, respectively, TCD in 76 of 97 and 66 of 67 patients, MRA in 95 of 97 and 66 of 67 patients. MRA and TCD collateral flow measurements via the anterior part of the circle of Willis yielded a sensitivity of 83 and 82%, a specificity of 77 and 79% and a similar accuracy of 80%. MRA and TCD collateral flow measurements via the posterior communicating artery yielded a sensitivity of 33 and 76%, a specificity of 88 and 47% and an accuracy of 47 and 68%. Conclusion: As compared with iaDSA, combined MRA and TCD has a moderate to good diagnostic value for the examination of intracranial collateral flow in patients with symptomatic carotid occlusion.
Association of Stearoyl-CoA Desaturase 1 Activity With Familial Combined Hyperlipidemia
Authors: Mar-Heyming Rebecca , Miyazaki Makoto , Weissglas-Volkov Daphna , Kolaitis Nicholas A. , Sadaat Narimaan , Plaisier Christopher , Pajukanta Päivi , Cantor Rita M. , de Bruin Tjerk W.A. , Ntambi James M. , Lusis Aldons J.
Published: March 2008

Objective—

Stearoyl-CoA desaturase 1 (SCD1) is the rate-limiting enzyme involved in the synthesis of monounsaturated fatty acids, and in mice SCD1 activity is associated with plasma triglyceride levels. We used the fatty acid desaturation index (the plasma ratio of 18:1/18:0) as a marker of SCD1 activity to investigate the relationship of SCD1 to familial combined hyperlipidemia (FCHL).

Methods and Results—

The fatty acid desaturation index was measured in 400 individuals from 18 extended FCHL pedigrees. FCHL-affected individuals exhibited increased SCD1 activity when compared to unrelated controls (
P
<0.0001). The fatty acid desaturation index was found to be highly heritable (h
2
=0.48,
P
=2.2×10
−11
) in this study sample. QTL analysis in 346 sibling pairs from 18 FCHL families revealed suggestive linkage of the desaturation index to chromosomes 3p26.1 to 3p13 (z=2.7,
P
=0.003), containing the peroxisome proliferator-activated receptor gamma (PPARγ) gene, and 20p11.21 to 20q13.32 (z=1.7,
P
=0.04), containing the hepatocyte nuclear factor 4, alpha (HNF4α) gene. A specific haplotype of HNF4α was found to be associated with the desaturation index in these FCHL families (
P
=0.002).

Conclusion—

Our results demonstrate that the fatty acid desaturation index is a highly heritable trait that is associated with the dyslipidemia observed in FCHL.
What Size Net Gives Valid Generalization?
Authors: Baum Eric B. , Haussler David
Published: March 2008

We address the question of when a network can be expected to generalize from m random training examples chosen from some arbitrary probability distribution, assuming that future test examples are drawn from the same distribution. Among our results are the following bounds on appropriate sample vs. network size. Assume 0 < ∊ ≤ 1/8. We show that if m ≥ O(W/∊ log N/∊) random examples can be loaded on a feedforward network of linear threshold functions with N nodes and W weights, so that at least a fraction 1 − ∊/2 of the examples are correctly classified, then one has confidence approaching certainty that the network will correctly classify a fraction 1 − ∊ of future test examples drawn from the same distribution. Conversely, for fully-connected feedforward nets with one hidden layer, any learning algorithm using fewer than Ω(W/∊) random training examples will, for some distributions of examples consistent with an appropriate weight choice, fail at least some fixed fraction of the time to find a weight choice that will correctly classify more than a 1 − ∊ fraction of the future test examples.
Modular Construction of Time-Delay Neural Networks for Speech Recognition
Authors: Waibel Alex
Published: March 2008

Several strategies are described that overcome limitations of basic network models as steps towards the design of large connectionist speech recognition systems. The two major areas of concern are the problem of time and the problem of scaling. Speech signals continuously vary over time and encode and transmit enormous amounts of human knowledge. To decode these signals, neural networks must be able to use appropriate representations of time and it must be possible to extend these nets to almost arbitrary sizes and complexity within finite resources. The problem of time is addressed by the development of a Time-Delay Neural Network; the problem of scaling by Modularity and Incremental Design of large nets based on smaller subcomponent nets. It is shown that small networks trained to perform limited tasks develop time invariant, hidden abstractions that can subsequently be exploited to train larger, more complex nets efficiently. Using these techniques, phoneme recognition networks of increasing complexity can be constructed that all achieve superior recognition performance.
A Silicon Model Of Auditory Localization
Authors: Lazzaro John , Mead Carver A.
Published: March 2008

The barn owl accurately localizes sounds in the azimuthal plane, using interaural time difference as a cue. The time-coding pathway in the owl's brainstem encodes a neural map of azimuth, by processing interaural timing information. We have built a silicon model of the time-coding pathway of the owl. The integrated circuit models the structure as well as the function of the pathway; most subcircuits in the chip have an anatomical correlate. The chip computes all outputs in real time, using analog, continuous-time processing.
New Models for Motor Control
Authors: Altman Jennifer S. , Kien Jenny
Published: March 2008

What do a prototype robot (Brooks 1989) and a model for the control of behavioral choice in insects (Altman and Kien 1987a) have in common? And what do they share with a scratching cat (Shadmehr 1989)? The answer is distributed control systems that do not depend on a central command center for the execution of behavioral outputs. The first two in particular are examples of a growing trend to replace the long-held concept of linear hierarchical control of motor output with one of decentralized, distributed control, with inputs at many levels and the output a consensus of the activity in several centers.

Brooks (1989) describes a six-legged machine that, in its most advanced form, can walk over rough terrain and prowl around following a source of warmth, such as a person. The six legs, chosen as a compromise between stability and ease of coordination, give the robot a superficial resemblance to an insect — but the similarity goes deeper. The modular control system, designed strictly on engineering principles for maximum efficiency and economy, bears a striking similarity to the model we have proposed elsewhere (Altman and Kien 1987a) to describe the organization of the motor system in insects such as the locust. In both systems, the same set of components can generate different behaviors, depending on the context, and similar principles govern the generation of different levels of behavior, from movements of a single leg to coordinated responses of the whole beast. Neither requires a single center for integrating all sensory information and conflicts tend to be resolved by consensus at the motor level.
Seeing Chips: Analog VLSI Circuits for Computer Vision
Authors: Koch Christof
Published: March 2008

Vision is simple. We open our eyes and, instantly, the world surrounding us is perceived in all its splendor. Yet Artificial Intelligence has been trying with very limited success for over 20 years to endow machines with similar abilities. A large van, filled with computers and driving unguided at a mile per hour across gently sloping hills in Colorado and using a laser-range system to “see” is the most we have accomplished so far. On the other hand, computers can play a decent game of chess or prove simple mathematical theorems. It is ironic that we are unable to reproduce perceptual abilities which we share with most animals while some of the features distinguishing us from even our closest cousins, chimpanzees, can be carried out by machines. Vision is difficult.
Unsupervised Learning
Authors: Barlow H.B.
Published: March 2008

What use can the brain make of the massive flow of sensory information that occurs without any associated rewards or punishments? This question is reviewed in the light of connectionist models of unsupervised learning and some older ideas, namely the cognitive maps and working models of Tolman and Craik, and the idea that redundancy is important for understanding perception (Attneave 1954), the physiology of sensory pathways (Barlow 1959), and pattern recognition (Watanabe 1960). It is argued that (1) The redundancy of sensory messages provides the knowledge incorporated in the maps or models. (2) Some of this knowledge can be obtained by observations of mean, variance, and covariance of sensory messages, and perhaps also by a method called “minimum entropy coding.” (3) Such knowledge may be incorporated in a model of “what usually happens” with which incoming messages are automatically compared, enabling unexpected discrepancies to be immediately identified. (4) Knowledge of the sort incorporated into such a filter is a necessary prerequisite of ordinary learning, and a representation whose elements are independent makes it possible to form associations with logical functions of the elements, not just with the elements themselves.
The Vapnik-Chervonenkis Dimension: Information versus Complexity in Learning
Authors: Abu-Mostafa Yaser S.
Published: March 2008

When feasible, learning is a very attractive alternative to explicit programming. This is particularly true in areas where the problems do not lend themselves to systematic programming, such as pattern recognition in natural environments. The feasibility of learning an unknown function from examples depends on two questions:

1. Do the examples convey enough information to determine the function? 2. Is there a speedy way of constructing the function from the examples?

These questions contrast the roles of information and complexity in learning. While the two roles share some ground, they are conceptually and technically different. In the common language of learning, the information question is that of generalization and the complexity question is that of scaling. The work of Vapnik and Chervonenkis (1971) provides the key tools for dealing with the information issue. In this review, we develop the main ideas of this framework and discuss how complexity fits in.
A Winner-Take-All Mechanism Based on Presynaptic Inhibition Feedback
Authors: Yuille Alan L. , Grzywacz Norberto M.
Published: March 2008

A winner-take-all mechanism is a device that determines the identity and amplitude of its largest input (Feldman and Ballard 1982). Such mechanisms have been proposed for various brain functions. For example, a theory for visual velocity estimate (Grzywacz and Yuille 1989) postulates that a winner-take-all selects the strongest responding cell in the cortex's middle temporal area (MT). This theory proposes a circuitry that links the directionally selective cells in the primary visual cortex to MT cells, making them velocity selective. Generally, several velocity cells would respond, but only the winner would determine the perception. In another theory, a winner-take-all guides the spotlight of attention to the most salient image part (Koch and Ullman 1985). Also, such mechanisms improve the signal-to-noise ratios of VLSI emulations of brain functions (Lazzaro and Mead 1989). Although computer algorithms for winner-take-all mechanisms exist (Feldman and Ballard 1982; Koch and Ullman 1985), good biologically motivated models do not. A candidate for a biological mechanism is lateral (mutual) inhibition (Hartline and Ratliff 1957). In some theoretical mutual-inhibition networks, the inhibition sums linearly to the excitatory inputs and the result is passed through a threshold non linearity (Hadeler 1974). However, these networks work only if the difference between winner and losers is large (Koch and Ullman 1985). We propose an alternative network, in which the output of each element feeds back to inhibit the inputs to other elements. The action of this presynaptic inhibition is nonlinear with a possible biophysical substrate. This paper shows that the new network converges stably to a solution that both relays the winner's identity and amplitude and suppresses information on the losers with arbitrary precision. We prove these results mathematically and illustrate the effectiveness of the network and some of its variants by computer simulations.
An Analysis of the Elastic Net Approach to the Traveling Salesman Problem
Authors: Durbin Richard , Szeliski Richard , Yuille Alan
Published: March 2008

This paper analyzes the elastic net approach (Durbin and Willshaw 1987) to the traveling salesman problem of finding the shortest path through a set of cities. The elastic net approach jointly minimizes the length of an arbitrary path in the plane and the distance between the path points and the cities. The tradeoff between these two requirements is controlled by a scale parameter K. A global minimum is found for large K, and is then tracked to a small value. In this paper, we show that (1) in the small K limit the elastic path passes arbitrarily close to all the cities, but that only one path point is attracted to each city, (2) in the large K limit the net lies at the center of the set of cities, and (3) at a critical value of K the energy function bifurcates. We also show that this method can be interpreted in terms of extremizing a probability distribution controlled by K. The minimum at a given K corresponds to the maximum a posteriori (MAP) Bayesian estimate of the tour under a natural statistical interpretation. The analysis presented in this paper gives us a better understanding of the behavior of the elastic net, allows us to better choose the parameters for the optimization, and suggests how to extend the underlying ideas to other domains.
The Storage of Time Intervals Using Oscillating Neurons
Authors: Miall Christopher
Published: March 2008

A mechanism to store and recall time intervals ranging from hundreds of milliseconds to tens of seconds is described. The principle is based on beat frequencies between oscillating elements; any small group of oscillators codes specifically for an interval equal to the lowest common multiple of their oscillation periods. This mechanism could be realized in the nervous system by an output neuron, excited by a group of pacemaker neurons, and able to select via a Hebbian rule a subgroup of pacemaker cells to encode any given interval, or small number of intervals (for example, a pattern of pulses). Recall could be achieved by resetting the pacemaker cells and setting a threshold for activation of the output unit. A simulation is described and the main features of such an encoding scheme are discussed.
Finite State Automata and Simple Recurrent Networks
Authors: Cleeremans Axel , Servan-Schreiber David , McClelland James L.
Published: March 2008

We explore a network architecture introduced by Elman (1988) for predicting successive elements of a sequence. The network uses the pattern of activation over a set of hidden units from time-step t−1, together with element t, to predict element t + 1. When the network is trained with strings from a particular finite-state grammar, it can learn to be a perfect finite-state recognizer for the grammar. When the network has a minimal number of hidden units, patterns on the hidden units come to correspond to the nodes of the grammar, although this correspondence is not necessary for the network to act as a perfect finite-state recognizer. We explore the conditions under which the network can carry information about distant sequential contingencies across intervening elements. Such information is maintained with relative ease if it is relevant at each intermediate step; it tends to be lost when intervening elements do not depend on it. At first glance this may suggest that such networks are not relevant to natural language, in which dependencies may span indefinite distances. However, embeddings in natural language are not completely independent of earlier information. The final simulation shows that long distance sequential contingencies can be encoded by the network even if only subtle statistical properties of embedded strings depend on the early information.
Learning in Artificial Neural Networks: A Statistical Perspective
Authors: White Halbert
Published: March 2008

The premise of this article is that learning procedures used to train artificial neural networks are inherently statistical techniques. It follows that statistical theory can provide considerable insight into the properties, advantages, and disadvantages of different network learning methods. We review concepts and analytical results from the literatures of mathematical statistics, econometrics, systems identification, and optimization theory relevant to the analysis of learning in artificial neural networks. Because of the considerable variety of available learning procedures and necessary limitations of space, we cannot provide a comprehensive treatment. Our focus is primarily on learning procedures for feedforward networks. However, many of the concepts and issues arising in this framework are also quite broadly relevant to other network learning paradigms. In addition to providing useful insights, the material reviewed here suggests some potentially useful new training methods for artificial neural networks.
Approximation of Boolean Functions by Sigmoidal Networks: Part I: XOR and Other Two-Variable Functions
Authors: Blum E. K.
Published: March 2008

We prove the existence of a manifold of exact solutions (mean-square error E = 0) of weights and thresholds for sigmoidal networks for XOR and other 2-variable Boolean functions. We also prove the existence of a manifold of local minima of E where E ≠ 0.
Coherent Compound Motion: Corners and Nonrigid Configurations
Authors: Zucker Steven W. , Iverson Lee , Hummel Robert A.
Published: March 2008

Consider two wire gratings, superimposed and moving across each other. Under certain conditions the two gratings will cohere into a single, compound pattern, which will appear to be moving in another direction. Such coherent motion patterns have been studied for sinusoidal component gratings, and give rise to percepts of rigid, planar motions. In this paper we show how to construct coherent motion displays that give rise to nonuniform, nonrigid, and nonplanar percepts. Most significantly, they also can define percepts with corners. Since these patterns are more consistent with the structure of natural scenes than rigid sinusoidal gratings, they stand as interesting stimuli for both computational and physiological studies. To illustrate, our display with sharp corners (tangent discontinuities or singularities) separating regions of coherent motion suggests that smoothing does not cross tangent discontinuities, a point that argues against existing (regularization) algorithms for computing motion. This leads us to consider how singularities can be confronted directly within optical flow computations, and we conclude with two hypotheses: (1) that singularities are represented within the motion system as multiple directions at the same retinotopic location; and (2) for component gratings to cohere, they must be at the same depth from the viewer. Both hypotheses have implications for the neural computation of coherent motion.
Modeling Orientation Discrimination at Multiple Reference Orientations with a Neural Network
Authors: Devos M. , Orban G. A.
Published: March 2008

We trained a multilayer perceptron with backpropagation to perform stimulus orientation discrimination at multiple references using biologically plausible values as input and output. Hidden units are necessary for good performance only when the network must operate at multiple reference orientations. The orientation tuning curves of the hidden units change with reference.

Our results suggest that at least for simple parameter discriminations such as orientation discrimination, one of the main functions of further processing in the visual system beyond striate cortex is to combine signals representing stimulus and reference.
The Upstart Algorithm: A Method for Constructing and Training Feedforward Neural Networks
Authors: Frean Marcus
Published: March 2008

A general method for building and training multilayer perceptrons composed of linear threshold units is proposed. A simple recursive rule is used to build the structure of the network by adding units as they are needed, while a modified perceptron algorithm is used to learn the connection strengths. Convergence to zero errors is guaranteed for any boolean classification on patterns of binary variables. Simulations suggest that this method is efficient in terms of the numbers of units constructed, and the networks it builds can generalize over patterns not in the training set.
A Neural Network for Nonlinear Bayesian Estimation in Drug Therapy
Authors: Shadmehr Reza , D'Argenio David Z.
Published: March 2008

The feasibility of developing a neural network to perform nonlinear Bayesian estimation from sparse data is explored using an example from clinical pharmacology. The problem involves estimating parameters of a dynamic model describing the pharmacokinetics of the bronchodilator theophylline from limited plasma concentration measurements of the drug obtained in a patient. The estimation performance of a backpropagation trained network is compared to that of the maximum likelihood estimator as well as the maximum a posteriori probability estimator. In the example considered, the estimator prediction errors (model parameters and outputs) obtained from the trained neural network were similar to those obtained using the nonlinear Bayesian estimator.
A Dynamic Neural Network Model of Sensorimotor Transformations in the Leech
Authors: Lockery Shawn R. , Fang Yan , Sejnowski Terrence J.
Published: March 2008

Interneurons in leech ganglia receive multiple sensory inputs and make synaptic contacts with many motor neurons. These “hidden” units coordinate several different behaviors. We used physiological and anatomical constraints to construct a model of the local bending reflex. Dynamic networks were trained on experimentally derived input-output patterns using recurrent backpropagation. Units in the model were modified to include electrical synapses and multiple synaptic time constants. The properties of the hidden units that emerged in the simulations matched those in the leech. The model and data support distributed rather than localist representations in the local bending reflex. These results also explain counterintuitive aspects of the local bending circuitry.
Small Depth Polynomial Size Neural Networks
Authors: Obradovic Zoran , Yan Peiyuan
Published: March 2008

For polynomially bounded weights and polylogarithmic precision, analog neural networks of polynomial size and depth 3 are strictly more powerful than those of polynomial size and depth 2.
Active Perception and Reinforcement Learning
Authors: Whitehead Steven D. , Ballard Dana H.
Published: March 2008

This paper considers adaptive control architectures that integrate active sensorimotor systems with decision systems based on reinforcement learning. One unavoidable consequence of active perception is that the agent's internal representation often confounds external world states. We call this phenomenon perceptual aliasing and show that it destabilizes existing reinforcement learning algorithms with respect to the optimal decision policy. A new decision system that overcomes these difficulties is described. The system incorporates a perceptual subcycle within the overall decision cycle and uses a modified learning algorithm to suppress the effects of perceptual aliasing. The result is a control architecture that learns not only how to solve a task but also where to focus its attention in order to collect necessary sensory information.
Deciphering the Brain's Codes
Authors: Konishi Masakazu
Published: March 2008

The two sensory systems discussed in this review use similar algorithms for the synthesis of the neuronal selectivity for the stimulus that releases a particular behavior, although the neural circuits, the brain sites involved, and even the species are different. This stimulus selectivity emerges gradually in a neural network organized according to parallel and hierarchical design principles. The parallel channels contain lower order stations with special circuits for the creation of neuronal selectivities for different features of the stimulus. Convergence of the parallel pathways brings these selectivities together at a higher order station for the eventual synthesis of the selectivity for the whole stimulus pattern. The neurons that are selective for the stimulus are at the top of the hierarchy, and they form the interface between the sensory and motor systems or between sensory systems of different modalities. The similarities of these two systems at the level of algorithms suggest the existence of rules of signal processing that transcend different sensory systems and species of animals.
A Biologically Supported Error-Correcting Learning Rule
Authors: Hancock Peter J. B. , Smith Leslie S. , Phillips William A.
Published: March 2008

We show that a form of synaptic plasticity recently discovered in slices of the rat visual cortex (Artola et al. 1990) can support an error-correcting learning rule. The rule increases weights when both pre- and postsynaptic units are highly active, and decreases them when pre-synaptic activity is high and postsynaptic activation is less than the threshold for weight increment but greater than a lower threshold. We show that this rule corrects false positive outputs in feedforward associative memory, that in an appropriate opponent-unit architecture it corrects misses, and that it performs better than the optimal Hebbian learning rule reported by Willshaw and Dayan (1990).
Constrained Nets for Graph Matching and Other Quadratic Assignment Problems
Authors: Simić Petar D.
Published: March 2008

Some time ago Durbin and Willshaw proposed an interesting parallel algorithm (the “elastic net”) for approximately solving some geometric optimization problems, such as the Traveling Salesman Problem. Recently it has been shown that their algorithm is related to neural networks of Hopfield and Tank, and that they both can be understood as the semiclassical approximation to statistical mechanics of related physical models. The main point of the elastic net algorithm is seen to be in the way one deals with the constraints when evaluating the effective cost function (free energy in the thermodynamic analogy), and not in its geometric foundation emphasized originally by Durbin and Willshaw. As a consequence, the elastic net algorithm is a special case of the more general physically based computations and can be generalized to a large class of nongeometric problems. In this paper we further elaborate on this observation, and generalize the elastic net to the quadratic assignment problem. We work out in detail its special case, the graph matching problem, because it is an important problem with many applications in computational vision and neural modeling. Simulation results on random graphs, and on structured (hand-designed) graphs of moderate size (20-100 nodes) are discussed.
Removing Time Variation with the Anti-Hebbian Differential Synapse
Authors: Mitchison Graeme
Published: March 2008

I describe a local synaptic learning rule that can be used to remove the effects of certain types of systematic temporal variation in the inputs to a unit. According to this rule, changes in synaptic weight result from a conjunction of short-term temporal changes in the inputs and the output. Formally,

This is like the differential rule proposed by Klopf (1986) and Kosko (1986), except for a change of sign, which gives it an anti-Hebbian character. By itself this rule is insufficient. A weight conservation condition is needed to prevent the weights from collapsing to zero, and some further constraint—implemented here by a biasing term—to select particular sets of weights from the subspace of those which give minimal variation. As an example, I show that this rule will generate center-surround receptive fields that remove temporally varying linear gradients from the inputs.
Simulations of a Reconstructed Cerebellar Purkinje Cell Based on Simplified Channel Kinetics
Authors: Bush Paul C. , Sejnowski Terrence J.
Published: March 2008

When cerebellar Purkinje cells are depolarized with a constant current pulse injected at the soma, complex spike discharge patterns are observed (Llinas and Sugimori 1980b). A computer model has been constructed to analyze how the Purkinje cell ionic conductance identified to date interact to produce the observed firing behavior. The kinetics of voltage-dependent conductance used in the model were significantly simpler than Hodgkin-Huxley kinetics, which have many parameters that must be experimentally determined. Our simplified scheme was able to reproduce the complex nonlinear responses found in real Purkinje cells. A similar approach could be used to study the wide variety of neurons found in different brain regions.
On the Mechanisms Underlying Directional Selectivity
Authors: Öğmen H.
Published: March 2008

Recent efforts in the understanding of motion detection and directional selectivity include electrophysiological studies using single photoreceptor stimulations and a combination of electrophysiology and neuropharmacology. Results of the former have been interpreted in favor of facilitator motion detection models while results of the latter have been interpreted in favor of inhibitory models. In this paper, this conflicting data interpretation problem is addressed by mathematically modeling some effects of neuropharmacological substances and by applying this formalism to a neural network model of directionally selective motion perception. The study offers a possible resolution to the paradox.
The Transition to Perfect Generalization in Perceptrons
Authors: Baum Eric B. , Lyuu Yuh-Dauh
Published: March 2008

Several recent papers (Gardner and Derrida 1989; Györgyi 1990; Sompolinsky et al. 1990) have found, using methods of statistical physics, that a transition to perfect generalization occurs in training a simple perceptron whose weights can only take values ±1. We give a rigorous proof of such a phenomena. That is, we show, for α = 2.0821, that if at least αn examples are drawn from the uniform distribution on {+1, −1}n and classified according to a target perceptron wt ∈ {+1, −1}n as positive or negative according to whether wt·x is nonnegative or negative, then the probability is 2−(√n) that there is any other such perceptron consistent with the examples. Numerical results indicate further that perfect generalization holds for α as low as 1.5.
Learning by Asymmetric Parallel Boltzmann Machines
Authors: Apolloni Bruno , Falco Diego de
Published: March 2008

We consider the Little, Shaw, Vasudevan model as a parallel asymmetric Boltzmann machine, in the sense that we extend to this model the entropic learning rule first studied by Ackley, Hinton, and Sejnowski in the case of a sequentially activated network with symmetric synaptic matrix. The resulting Hebbian learning rule for the parallel asymmetric model draws the signal for the updating of synaptic weights from time averages of the discrepancy between expected and actual transitions along the past history of the network. As we work without the hypothesis of symmetry of the weights, we can include in our analysis also feedforward networks, for which the entropic learning rule turns out to be complementary to the error backpropagation rule, in that it “rewards the correct behavior” instead of “penalizing the wrong answers.”
Generalization Effects of k-Neighbor Interpolation Training
Authors: Kawabata Takeshi
Published: March 2008

This paper describes a new training method for a continuous mapping and/or pattern classification neural network that performs local sample-density smoothing. A conventional training method uses point-to-point mapping from an input space to an output space. Even though the mapping may be precise at two given training sample points, there are no guarantees of mapping accuracy at points on a line segment connecting the sample points. This paper first discusses a theory for formulating line-to-line mapping. The theory is called interpolation training. This paper then expands the theory to k-nearest neighbor interpolation. The k-neighbor interpolation training (KNIT) method connects an input sample training point to its k-neighbor points via k line segments. Then, the method maps these k line segments in the input space for each training sample to linear line segments in the output space that interpolate between training output values. Thus, a web structure made by connecting input samples is mapped into the same structure in an output space. The KNIT method reduces the over learning problem caused by point-to-point training by smoothing input/output functions. Simulation tasks show that KNIT improves vowel recognition on a small speech database.
On the Characteristics of the Autoassociative Memory with Nonzero-Diagonal Terms in the Memory Matrix
Authors: Wang Jung-Hua , Krile Thomas F. , Walkup John F. , Jong Tai-Lang
Published: March 2008

A statistical method is applied to explore the unique characteristics of a certain class of neural network autoassociative memory with N neurons and first-order synaptic interconnections. The memory matrix is constructed to store M = αN vectors based on the outer-product learning algorithm. We theoretically prove that, by setting all the diagonal terms of the memory matrix to be M and letting the input error ratio ρ = 0, the probability of successful recall Pr steadily decreases as α increases, but as α increases past 1.0, Pr begins to increase slowly. When 0 < ρ ≤ 0.5, the network exhibits strong error-correction capability if α ≤ 0.15 and this capability is shown to rapidly decrease as α increases. The network essentially loses all its error-correction capability at α = 2, regardless of the value of ρ. When 0 < ρ ≤ 0.5, and under the constraint of Pr > 0.99, the tradeoff between the number of stable states and their attraction force is analyzed and the memory capacity is shown to be 0.15N at best.
Neural Network Classifiers Estimate Bayesian a posteriori Probabilities
Authors: Richard Michael D. , Lippmann Richard P.
Published: March 2008

Many neural network classifiers provide outputs which estimate Bayesian a posteriori probabilities. When the estimation is accurate, network outputs can be treated as probabilities and sum to one. Simple proofs show that Bayesian probabilities are estimated when desired network outputs are 1 of M (one output unity, all others zero) and a squared-error or cross-entropy cost function is used. Results of Monte Carlo simulations performed using multilayer perceptron (MLP) networks trained with backpropagation, radial basis function (RBF) networks, and high-order polynomial networks graphically demonstrate that network outputs provide good estimates of Bayesian probabilities. Estimation accuracy depends on network complexity, the amount of training data, and the degree to which training data reflect true likelihood distributions and a priori class probabilities. Interpretation of network outputs as Bayesian probabilities allows outputs from multiple networks to be combined for higher level decision making, simplifies creation of rejection thresholds, makes it possible to compensate for differences between pattern class probabilities in training and test data, allows outputs to be used to minimize alternative risk functions, and suggests alternative measures of network performance.
Speaker-Independent Digit Recognition Using a Neural Network with Time-Delayed Connections
Authors: Unnikrishnan K. P. , Hopfield J. J. , Tank D. W.
Published: March 2008

The capability of a small neural network to perform speaker-independent recognition of spoken digits in connected speech has been investigated. The network uses time delays to organize rapidly changing outputs of symbol detectors over the time scale of a word. The network is data driven and unclocked. To achieve useful accuracy in a speaker-independent setting, many new ideas and procedures were developed. These include improving the feature detectors, self-recognition of word ends, reduction in network size, and dividing speakers into natural classes. Quantitative experiments based on Texas Instruments (TI) digit databases are described.
Local Feedback Multilayered Networks
Authors: Frasconi Paolo , Gori Marco , Soda Giovanni
Published: March 2008

In this paper, we investigate the capabilities of local feedback multilayered networks, a particular class of recurrent networks, in which feedback connections are only allowed from neurons to themselves. In this class, learning can be accomplished by an algorithm that is local in both space and time. We describe the limits and properties of these networks and give some insights on their use for solving practical problems.