bluuebunny/crossref_metadata_2025 huggingface dataset:
Thyroid Hormone Receptors Suppress Pituitary Tumor Transforming Gene 1 Activity in Hepatoma
Authors: Chen Ruey-Nan , Huang Ya-Hui , Yeh Chau-Ting , Liao Chen-Hsin , Lin Kwang-Huei
Published: March 2008

Pituitary tumor transforming gene 1 (PTTG1) is expressed in most tumors. However, whether thyroid hormone (T3) and its receptors (TR) regulate PTTG1 in human hepatocellular carcinomas (HCC) remains unclear. Previous cDNA microarrays revealed PTTG1 is down-regulated by T3/TR. This study investigated the significance of PTTG1 regulation by T3 in HCC cells. The PTTG1 mRNA and protein expression were repressed by T3 in HCC cell lines overexpressing TR. However, after knockdown of TRs expression by RNA interference, PTTG1 repression by T3 was abolished. Similar results were observed in thyroidectomized rats. To localize the regulatory region in the PTTG1 promoter, serial deletions within the PTTG1 promoter region were constructed. The promoter activity of the PTTG1 gene was repressed (25–51%) by T3. Additionally, these findings indicate that PTTG1 may be regulated by Sp1. The critical role of the −594 and −520 Sp1 binding sites was confirmed by electrophoretic mobility shift assay. Transfection with Sp1 expression vector enhanced the activity of the PTTG1 promoter fragment reporter. Also, Sp1 was down-regulated in HCC cells and in thyroidectomized rat after T3 treatment. Additionally, ectopic expression of PTTG1 promotes cell proliferation in Hep3B hepatoma cells. Conversely, knockdown of PTTG1 or Sp1 expression reduced cell proliferation in HepG2 cells. Notably, the expression of PTTG1 and Sp1 was inversely correlated with the expression of TR proteins in HCC. Together, these findings indicate that PTTG1 gene expression is mediated by Sp1 and is indirectly down-regulated by T3. Finally, overexpression of PTTG1 or SP1 in HCCs is TR-dependent and crucial in the development of HCC. [Cancer Res 2008;68(6):1697–1706]
Triggering Senescence Programs Suppresses Chk1 Kinase and Sensitizes Cells To Genotoxic Stresses
Authors: Gabai Vladimir L. , O'Callaghan-Sunol Cornelia , Meng Le , Sherman Michael Y. , Yaglom Julia
Published: March 2008

Depletion of the major heat shock protein Hsp72 leads to activation of the senescence program in a variety of tumor cell lines via both p53-dependent and p53-independent pathways. Here, we found that the Hsp72-depleted cells show defect in phosphorylation and activation of the protein kinase Chk1 by genotoxic stresses, such as UVC irradiation or camptothecin. Under these conditions, phosphorylation of Rad17 was also suppressed, whereas phosphorylation of p53 at Ser15 was not affected, indicating a specific defect in phosphorylation of a subset of the ATR kinase substrates. Similarly, suppression of Chk1 activation was seen when senescence signaling was triggered by direct stimulation of p53, depletion of Cdc2, or overexpression of the cell cycle inhibitors p21 or p16. Thus, defect in Chk1 activation was not a consequence of the chaperone imbalance, but rather a downstream effect of activation of the senescence signaling. Inhibition of Chk1 was associated with inefficient inter-S phase checkpoint, as Hsp72 depleted cells failed to halt cell cycle progression upon UVC irradiation. Accordingly, sensitivity of cells to genotoxic stimuli after Hsp72 depletion was significantly enhanced. Thus, activation of the senescence signaling causes a defect in the DNA damage response manifested in increased sensitivity to genotoxic stresses. [Cancer Res 2008;68(6):1834–42]
PIK3CA Mutation/PTEN Expression Status Predicts Response of Colon Cancer Cells to the Epidermal Growth Factor Receptor Inhibitor Cetuximab
Authors: Jhawer Minaxi , Goel Sanjay , Wilson Andrew J. , Montagna Cristina , Ling Yi-He , Byun Do-Sun , Nasser Shannon , Arango Diego , Shin Joongho , Klampfer Lidija , Augenlicht Leonard H. , Soler Roman Perez , Mariadason John M.
Published: March 2008

Cetuximab is a monoclonal antibody that targets the human epidermal growth factor receptor (EGFR). Although approved for use in EGFR-overexpressing advanced colorectal cancer, recent studies have shown a lack of association between EGFR overexpression and cetuximab response, requiring the identification of novel biomarkers predictive of response to this agent. To do so, 22 colon cancer cell lines were screened for cetuximab response in vitro and sensitive and resistant lines were identified. In sensitive cell lines, cetuximab induced a G0-G1 arrest without inducing apoptosis. Notably, cetuximab-sensitive but not cetuximab-resistant cell lines were preferentially responsive to EGF-stimulated growth. Whereas neither EGFR protein/mRNA expression nor gene copy number correlated with cetuximab response, examination of the mutation status of signaling components downstream of EGFR showed that cell lines with activating PIK3CA mutations or loss of PTEN expression (PTEN null) were more resistant to cetuximab than PIK3CA wild type (WT)/PTEN-expressing cell lines (14 ± 5.0% versus 38.5 ± 6.4% growth inhibition, mean ± SE; P = 0.008). Consistently, PIK3CA mutant isogenic HCT116 cells showed increased resistance to cetuximab compared with PIK3CA WT controls. Furthermore, cell lines that were PIK3CA mutant/PTEN null and Ras/BRAF mutant were highly resistant to cetuximab compared with those without dual mutations/PTEN loss (10.8 ± 4.3% versus 38.8 ± 5.9% growth inhibition, respectively; P = 0.002), indicating that constitutive and simultaneous activation of the Ras and PIK3CA pathways confers maximal resistance to this agent. A priori screening of colon tumors for PTEN expression status and PIK3CA and Ras/BRAF mutation status could help stratify patients likely to benefit from this therapy. [Cancer Res 2008;68(6):1953–60]
Therapy of Advanced Established Murine Breast Cancer with a Recombinant Adenoviral ErbB-2/neu Vaccine
Authors: Park Jong Myun , Terabe Masaki , Steel Jason C. , Forni Guido , Sakai Yoshio , Morris John C. , Berzofsky Jay A.
Published: March 2008

ErbB-2 (HER-2/neu) is a transforming oncogene expressed by a substantial fraction of breast cancers, and monoclonal antibody therapy directed toward this antigen is an established treatment modality. However, not all tumors respond, and with a monoclonal antibody directed to a single epitope, there is always the risk of tumor escape. Furthermore, passive antibody therapy requires continual treatment. Whereas cancer vaccines have prevented the growth of tumors, it has been far more difficult to treat large established tumors. Here, we show that vaccination with a recombinant adenovirus expressing a truncated ErbB-2 antigen can cure large established subcutaneous ErbB-2–expressing breast cancers in mice, and can also cure extensive established lung metastatic disease. We also show that the mechanism of protection involves antibody-mediated blockade of ErbB-2 function, independent of Fc receptors. We conclude that a vaccine inducing antibodies to a functional oncogenic receptor could have tremendous therapeutic potential against cancers overexpressing such molecules. [Cancer Res 2008;68(6):1979–87]
SOX9 Is Expressed in Human Fetal Prostate Epithelium and Enhances Prostate Cancer Invasion
Authors: Wang Hongyun , Leav Irwin , Ibaragi Soichiro , Wegner Michael , Hu Guo-fu , Lu Michael L. , Balk Steven P. , Yuan Xin
Published: March 2008

SOX9 is a transcription factor that plays a critical role in the development of multiple tissues. We previously reported that SOX9 in normal human adult prostate was restricted to basal epithelium. SOX9 was also expressed in a subset of prostate cancer (PCa) cells and was increased in relapsed hormone-refractory PCa. Moreover, SOX9 expression in PCa cell lines enhanced tumor cell proliferation and was β-catenin regulated. Here we report additional in vivo results showing that SOX9 is highly expressed during fetal prostate development by epithelial cells expanding into the mesenchyme, suggesting it may contribute to invasive growth in PCa. Indeed, SOX9 overexpression in LNCaP PCa xenografts enhanced growth, angiogenesis, and invasion. Conversely, short hairpin RNA–mediated SOX9 suppression inhibited the growth of CWR22Rv1 PCa xenografts. These results support important functions of SOX9 in both the development and maintenance of normal prostate, and indicate that these functions contribute to PCa tumor growth and invasion. [Cancer Res 2008;68(6):1625–30]
Diphenyl Difluoroketone: A Curcumin Derivative with Potent In vivo Anticancer Activity
Authors: Subramaniam Dharmalingam , May Randal , Sureban Sripathi M. , Lee Katherine B. , George Robert , Kuppusamy Periannan , Ramanujam Rama P. , Hideg Kalman , Dieckgraefe Brian K. , Houchen Courtney W. , Anant Shrikant
Published: March 2008

Diphenyl difluoroketone (EF24), a molecule having structural similarity to curcumin, was reported to inhibit proliferation of a variety of cancer cells in vitro. However, the efficacy and in vivo mechanism of action of EF24 in gastrointestinal cancer cells have not been investigated. Here, we assessed the in vivo therapeutic effects of EF24 on colon cancer cells. Using hexosaminidase assay, we determined that EF24 inhibits proliferation of HCT-116 and HT-29 colon and AGS gastric adenocarcinoma cells but not of mouse embryo fibroblasts. Furthermore, the cancer cells showed increased levels of activated caspase-3 and increased Bax to Bcl-2 and Bax to Bcl-xL ratios, suggesting that the cells were undergoing apoptosis. At the same time, cell cycle analysis showed that there was an increased number of cells in the G2-M phase. To determine the effects of EF24 in vivo, HCT-116 colon cancer xenografts were established in nude mice and EF24 was given i.p. EF24 significantly suppressed the growth of colon cancer tumor xenografts. Immunostaining for CD31 showed that there was a lower number of microvessels in the EF24-treated animals coupled with decreased cyclooxygenase-2, interleukin-8, and vascular endothelial growth factor mRNA and protein expression. Western blot analyses also showed decreased AKT and extracellular signal-regulated kinase activation in the tumors. Taken together, these data suggest that the novel curcumin-related compound EF24 is a potent antitumor agent that induces caspase-mediated apoptosis during mitosis and has significant therapeutic potential for gastrointestinal cancers. [Cancer Res 2008;68(6):1962–9]
GM1 and Tumor Necrosis Factor-α, Overexpressed in Renal Cell Carcinoma, Synergize to Induce T-Cell Apoptosis
Authors: Das Tanya , Sa Gaurisankar , Hilston Cynthia , Kudo Daisuke , Rayman Patricia , Biswas Kaushik , Molto Luis , Bukowski Ronald , Rini Brian , Finke James H. , Tannenbaum Charles
Published: March 2008

The ability to induce T-cell apoptosis is one mechanism by which tumors evade the immune system, although the molecules involved remain controversial. We found that renal cell carcinoma (RCC)–induced T-cell apoptosis was inhibited by >50% when cocultures were performed with ganglioside-depleted tumor cells, caspase-8–negative lymphocytes, or anti–tumor necrosis factor-α (TNFα) antibodies, suggesting that tumor gangliosides synergize with signals delivered through TNFα death receptors to mediate T-cell killing. The synergy between tumor-derived TNFα and the RCC-overexpressed ganglioside GM1 for killing resting T cells is corroborated by studies using purified GM1 and rTNFα, which indicate that a 48-hour pretreatment with the ganglioside optimally sensitizes the lymphocytes to a TNFα-induced apoptotic death. However, activated T cells, which synthesize TNFα themselves, can be killed by exogenous GM1 alone. RelA-overexpressing lymphocytes are protected from GM1 plus TNFα-mediated apoptosis, a finding consistent with our previous studies indicating that gangliosides inhibit nuclear factor-κB activation. These results are clinically relevant because, similar to T-cells cocultured with GM1-overexpressing RCC lines, T cells isolated from the peripheral blood of patients with metastatic RCC are also heavily coated with that tumor-shed ganglioside. This population of patient cells, unlike T cells isolated from normal donors, is highly susceptible to apoptosis induced by rTNFα or by metastatic patient sera, which contain elevated levels of the cytokine. This report thus extends our previous studies by demonstrating that tumor-derived TNFα enhances RCC apoptogenicity not only by inducing ganglioside synthesis but also by initiating receptor-dependent apoptosis in T cells in which the nuclear factor-κB activation pathway has been inhibited by GM1. [Cancer Res 2008;68(6):2014–23]
Silibinin Inhibits Colorectal Cancer Growth by Inhibiting Tumor Cell Proliferation and Angiogenesis
Authors: Singh Rana P. , Gu Mallikarjuna , Agarwal Rajesh
Published: March 2008

Herein, for the first time, we investigated in vivo efficacy and associated molecular biomarkers and mechanisms of a chemopreventive agent, silibinin, against human colorectal carcinoma (CRC) HT29 xenograft growth. Nude mice were implanted with HT29 cells and fed with vehicle (carboxymethyl cellulose or phosphatidylcholine) or 200 mg/kg/d dose of silibinin or 100 and 200 mg/kg/d doses of silybin-phytosome (5 days per week) for 32 days. Silibinin inhibited tumor growth that accounted for 48% (P = 0.002) decrease in tumor volume and 42% (P = 0.012) decrease in tumor weight at the end of the experiment without any adverse health effect. A stronger antitumor efficacy was observed with silybin-phytosome preparation. Silibinin decreased proliferation index by 40% (P < 0.001), increased apoptotic index by ∼2-fold (P = 0.001), and reduced microvessel density by 36% (P = 0.001) in tumors. Antiproliferative and proapoptotic effects of silibinin were associated with down-regulation of extracellular signal-regulated kinase 1/2 (ERK1/2) and Akt phosphorylation as well as cyclin D1 expression. Antiangiogenic effect of silibinin was coupled with a strong decrease in inducible nitric oxide synthase (NOS) and NOS3, cyclooxygenase-1 (COX-1) and COX-2, and hypoxia-inducing factor-1α (HIF-1α) and vascular endothelial growth factor (VEGF). These findings suggest in vivo antitumor efficacy of silibinin against CRC involving its antiproliferative, proapoptotic, and antiangiogenic activities. The inhibition of ERK1/2 and Akt signaling may account for antiproliferative and proapoptotic effects, whereas down-regulation of NOS, COX, HIF-1α, and VEGF expression could lead to antiangiogenic effect of silibinin against CRC. Overall, potential use of silibinin against human CRC could be suggested. [Cancer Res 2008;68(6):2043–50]
Promoter Methylation of Genes in and around the Candidate Lung Cancer Susceptibility Locus6q23-25
Authors: Tessema Mathewos , Willink Randy , Do Kieu , Yu Yang Y. , Yu Wayne , Machida Emi O. , Brock Malcolm , Van Neste Leander , Stidley Christine A. , Baylin Stephen B. , Belinsky Steven A.
Published: March 2008

Chromosomal aberrations associated with lung cancer are frequently observed in the long arm of chromosome 6. A candidate susceptibility locus at 6q23-25 for lung cancer was recently identified; however, no tumor suppressor genes inactivated by mutation have been identified in this locus. Genetic, epigenetic, gene expression, and in silico screening approaches were used to select 43 genes located in 6q12-27 for characterization of methylation status. Twelve (28%) genes were methylated in at least one lung cancer cell line, and methylation of 8 genes was specific to lung cancer cell lines. Five of the 8 genes with the highest prevalence for methylation in cell lines (TCF21, SYNE1, AKAP12, IL20RA, and ACAT2) were examined in primary lung adenocarcinoma samples from smokers (n = 100) and never smokers (n = 75). The prevalence for methylation of these genes was 81%, 50%, 39%, 26%, and 14%, respectively, and did not differ by smoking status or age at diagnosis. Transcription of SYNE1, AKAP12, and IL20RA was completely silenced by hypermethylation and could be restored after treatment with 5-aza-2-deoxycytidine. Significant associations were found between methylation of SYNE1 and TCF21, SYNE1 and AKAP12, and AKAP12 and IL20RA, indicating a coordinated inactivation of these genes in tumors. A higher prevalence for methylation of these genes was not associated with early-onset lung cancer cases, most likely precluding their involvement in familial susceptibility to this disease. Together, our results indicate that frequent inactivation of multiple candidate tumor suppressor genes within chromosome 6q likely contributes to development of sporadic lung cancer. [Cancer Res 2008;68(6):1707–14]
Novel SN-38–Incorporated Polymeric Micelle, NK012, Strongly Suppresses Renal Cancer Progression
Authors: Sumitomo Makoto , Koizumi Fumiaki , Asano Takako , Horiguchi Akio , Ito Keiichi , Asano Tomohiko , Kakizoe Tadao , Hayakawa Masamichi , Matsumura Yasuhiro
Published: March 2008

It has been recently reported that NK012, a 7-ethyl-10-hydroxy-camptothecin (SN-38)–releasing nanodevice, markedly enhances the antitumor activity of SN-38, especially in hypervascular tumors through the enhanced permeability and retention effect. Renal cell carcinoma (RCC) is a typical hypervascular tumor with an irregular vascular architecture. We therefore investigated the antitumor activity of NK012 in a hypervascular tumor model from RCC. Immunohistochemical examination revealed that Renca tumors contained much more CD34-positive neovessels than SKRC-49 tumors. Compared with CPT-11, NK012 had significant antitumor activity against both bulky Renca and SKRC-49 tumors. Notably, NK012 eradicated rapid-growing Renca tumors in 6 of 10 mice, whereas it failed to eradicate SKRC-49 tumors. In the pulmonary metastasis treatment model, an enhanced and prolonged distribution of free SN-38 was observed in metastatic lung tissues but not in nonmetastatic lung tissues after NK012 administration. NK012 treatment resulted in a significant decrease in metastatic nodule number and was of benefit to survival. Our study shows the outstanding advantage of polymeric micelle-based drug carriers and suggests that NK012 would be effective in treating disseminated RCCs with irregular vascular architectures. [Cancer Res 2008;68(6):1631–5]
Mechanisms Underlying Caloric Restriction and Lifespan Regulation
Authors: Ungvari Zoltan , Parrado-Fernandez Cristina , Csiszar Anna , de Cabo Rafael
Published: March 2008

This review focuses on the emerging evidence that attenuation of the production of reactive oxygen species and inhibition of inflammatory pathways play a central role in the antiaging cardiovascular effects of caloric restriction. Particular emphasis is placed on the potential role of the plasma membrane redox system in caloric restriction–induced pathways responsible for sensing oxidative stress and increasing cellular oxidative stress resistance. We propose that caloric restriction increases bioavailability of NO, decreases vascular reactive oxygen species generation, activates the Nrf2/antioxidant response element pathway, inducing reactive oxygen species detoxification systems, exerts antiinflammatory effects, and, thereby, suppresses initiation/progression of vascular disease that accompany aging.
Review of Neural Networks for Speech Recognition
Authors: Lippmann Richard P.
Published: March 2008

The performance of current speech recognition systems is far below that of humans. Neural nets offer the potential of providing massive parallelism, adaptation, and new algorithmic approaches to problems in speech recognition. Initial studies have demonstrated that multilayer networks with time delays can provide excellent discrimination between small sets of pre-segmented difficult-to-discriminate words, consonants, and vowels. Performance for these small vocabularies has often exceeded that of more conventional approaches. Physiological front ends have provided improved recognition accuracy in noise and a cochlea filter-bank that could be used in these front ends has been implemented using micro-power analog VLSI techniques. Techniques have been developed to scale networks up in size to handle larger vocabularies, to reduce training time, and to train nets with recurrent connections. Multilayer perceptron classifiers are being integrated into conventional continuous-speech recognizers. Neural net architectures have been developed to perform the computations required by vector quantizers, static pattern classifiers, and the Viterbi decoding algorithm. Further work is necessary for large-vocabulary continuous-speech problems, to develop training algorithms that progressively build internal word models, and to develop compact VLSI neural net hardware.
A Multiple-Map Model for Pattern Classification
Authors: Rojer Alan , Schwartz Eric
Published: March 2008

A characteristic feature of vertebrate sensory cortex (and midbrain) is the existence of multiple two-dimensional map representations. Some workers have considered single-map classification (e.g. Kohonen 1984) but little work has focused on the use of multiple maps. We have constructed a multiple-map classifier, which permits abstraction of the computational properties of a multiple-map architecture. We identify three problems which characterize a multiple-map classifier: classification in two dimensions, mapping from high dimensions to two dimensions, and combination of multiple maps. We demonstrate component solutions to each of the problems, using Parzen-window density estimation in two dimensions, a generalized Fisher discriminant function for dimensionality reduction, and split/merge methods to construct a “tree of maps” for the multiple-map representation. The combination of components is modular and each component could be improved or replaced without affecting the other components. The classifier training procedure requires time linear in the number of training examples; classification time is independent of the number of training examples and requires constant space. Performance of this classifier on Fisher's iris data, Gaussian clusters on a five-dimensional simplex, and digitized speech data is comparable to competing algorithms, such as nearest-neighbor, back-propagation and Gaussian classifiers. This work provides an example of the computational utility of multiple-map representations for classification. It is one step towards the goal of understanding why brain areas such as visual cortex utilize multiple map-like representations of the world.
A Control Systems Model of Smooth Pursuit Eye Movements with Realistic Emergent Properties
Authors: Krauzlis R. J. , Lisberger S. G.
Published: March 2008

Visual tracking of objects in a noisy environment is a difficult problem that has been solved by the primate oculomotor system, but remains unsolved in robotics. In primates, smooth pursuit eye movements match eye motion to target motion to keep the eye pointed at smoothly moving targets. We have used computer models as a tool to investigate possible computational strategies underlying this behavior. Here, we present a model based upon behavioral data from monkeys. The model emphasizes the variety of visual signals available for pursuit and, in particular, includes a sensitivity to the acceleration of retinal images. The model was designed to replicate the initial eye velocity response observed during pursuit of different target motions. The strength of the model is that it also exhibits a number of emergent properties that are seen in the behavior of both humans and monkeys. This suggests that the elements in the model capture important aspects of the mechanism of visual tracking by the primate smooth pursuit system.
The Brain Binds Entities and Events by Multiregional Activation from Convergence Zones
Authors: Damasio Antonio R.
Published: March 2008

The experience of reality, in both perception and recall, is spatially and temporally coherent and “in-register.” Features are bound in entities, and entities are bound in events. The properties of these entities and events, however, are represented in many different regions of the brain that are widely separated. The degree of neural parcellation is even greater when we consider that the perception of most entities and events also requires a motor interaction on the part of the perceiver (such as eye movements and hand movements) and often includes a recordable modification of the perceiver's somatic state. The question of how the brain achieves integration starting with the bits and pieces it has to work with, is the binding problem. Here we propose a new solution for this problem, at the level of neural systems that integrate functional regions of the telencephalon.
Part Segmentation for Object Recognition
Authors: Pentland Alex
Published: March 2008

Visual object recognition is a difficult problem that has been solved by biological visual systems. An approach to object recognition is described in which the image is segmented into parts using two simple, biologically-plausible mechanisms: a filtering operation to produce a large set of potential object “parts,” followed by a new type of network that searches among these part hypotheses to produce the simplest, most likely description of the image's part structure.
Recurrent Backpropagation and the Dynamical Approach to Adaptive Neural Computation
Authors: Pineda Fernando J.
Published: March 2008

Error backpropagation in feedforward neural network models is a popular learning algorithm that has its roots in nonlinear estimation and optimization. It is being used routinely to calculate error gradients in nonlinear systems with hundreds of thousands of parameters. However, the classical architecture for backpropagation has severe restrictions. The extension of backpropagation to networks with recurrent connections will be reviewed. It is now possible to efficiently compute the error gradients for networks that have temporal dynamics, which opens applications to a host of problems in systems identification and control.
A Proposal for More Powerful Learning Algorithms
Authors: Baum Eric B.
Published: March 2008

Judd (1988) and Blum and Rivest (1988) have recently proved that the loading problem for neural networks is NP complete. This makes it very unlikely that any algorithm like backpropagation which varies weights on a network of fixed size and topology will be able to learn in polynomial time. However, Valiant has recently proposed a learning protocol (Valiant 1984), which allows one to sensibly consider generalization by learning algorithms with the freedom to add neurons and synapses, as well as simply adjusting weights. Within this context, standard circuit complexity arguments show that learning algorithms with such freedom can solve in polynomial time any learning problem that can be solved in polynomial time by any algorithm whatever. In this sense, neural nets are universal learners, capable of learning any learnable class of concepts.
A Possible Neural Mechanism for Computing Shape From Shading
Authors: Pentland Alex
Published: March 2008

A simple neural mechanism that recovers surface shape from image shading is derived from a simplified model of the physics of image formation. The mechanism's performance is surprisingly good even when applied to complex natural images, and is even able to extract significant shape information from some line drawings.
Optimization in Model Matching and Perceptual Organization
Authors: Mjolsness Eric , Gindi Gene , Anandan P.
Published: March 2008

We introduce an optimization approach for solving problems in computer vision that involve multiple levels of abstraction. Our objective functions include compositional and specialization hierarchies. We cast vision problems as inexact graph matching problems, formulate graph matching in terms of constrained optimization, and use analog neural networks to perform the optimization. The method is applicable to perceptual grouping and model matching. Preliminary experimental results are shown.
Distributed Parallel Processing in the Vestibulo-Oculomotor System
Authors: Anastasio Thomas J. , Robinson David A.
Published: March 2008

The mechanisms of eye-movement control are among the best understood in motor neurophysiology. Detailed anatomical and physiological data have paved the way for theoretical models that have unified existing knowledge and suggested further experiments. These models have generally taken the form of black-box diagrams (for example, Robinson 1981) representing the flow of hypothetical signals between idealized signal-processing blocks. They approximate overall oculomotor behavior but indicate little about how real eye-movement signals would be carried and processed by real neural networks. Neurons that combine and transmit oculomotor signals, such as those in the vestibular nucleus (VN), actually do so in a diverse, seemingly random way that would be impossible to predict from a block diagram. The purpose of this study is to use a neural-network learning scheme (Rumelhart et al. 1986) to construct parallel, distributed models of the vestibulo-oculomotor system that simulate the diversity of responses recorded experimentally from VN neurons.
A Robot that Walks; Emergent Behaviors from a Carefully Evolved Network
Authors: Brooks Rodney A.
Published: March 2008

Most animals have significant behavioral expertise built in without having to explicitly learn it all from scratch. This expertise is a product of evolution of the organism; it can be viewed as a very long-term form of learning which provides a structured system within which individuals might learn more specialized skills or abilities. This paper suggests one possible mechanism for analagous robot evolution by describing a carefully designed series of networks, each one being a strict augmentation of the previous one, which control a six-legged walking machine capable of walking over rough terrain and following a person passively sensed in the infrared spectrum. As the completely decentralized networks are augmented, the robot's performance and behavior repertoire demonstrably improve. The rationale for such demonstrations is that they may provide a hint as to the requirements for automatically building massive networks to carry out complex sensory-motor tasks. The experiments with an actual robot ensure that an essence of reality is maintained and that no critical disabling problems have been ignored.
A Learning Algorithm for Continually Running Fully Recurrent Neural Networks
Authors: Williams Ronald J. , Zipser David
Published: March 2008

The exact form of a gradient-following learning algorithm for completely recurrent networks running in continually sampled time is derived and used as the basis for practical algorithms for temporal supervised learning tasks. These algorithms have (1) the advantage that they do not require a precisely defined training interval, operating while the network runs; and (2) the disadvantage that they require nonlocal communication in the network being trained and are computationally expensive. These algorithms allow networks having recurrent connections to learn complex tasks that require the retention of information over time periods having either fixed or indefinite length.
Fast Learning in Networks of Locally-Tuned Processing Units
Authors: Moody John , Darken Christian J.
Published: March 2008

We propose a network architecture which uses a single internal layer of locally-tuned processing units to learn both classification tasks and real-valued function approximations (Moody and Darken 1988). We consider training such networks in a completely supervised manner, but abandon this approach in favor of a more computationally efficient hybrid learning method which combines self-organized and supervised learning. Our networks learn faster than backpropagation for two reasons: the local representations ensure that only a few units respond to any given input, thus reducing computational overhead, and the hybrid learning rules are linear rather than nonlinear, thus leading to faster convergence. Unlike many existing methods for data analysis, our network architecture and learning rules are truly adaptive and are thus appropriate for real-time use.
Neurogammon Wins Computer Olympiad
Authors: Tesauro Gerald
Published: March 2008

Neurogammon 1.0 is a backgammon program which uses multilayer neural networks to make move decisions and doubling decisions. The networks learned to play backgammon by backpropagation training on expert data sets. At the recently held First Computer Olympiad in London, Neurogammon won the backgammon competition with a perfect record of five wins and no losses, thereby becoming the first learning program ever to win a tournament.
Asymptotic Convergence of Backpropagation
Authors: Tesauro Gerald , He Yu , Ahmad Subutai
Published: March 2008

We calculate analytically the rate of convergence at long times in the backpropagation learning algorithm for networks with and without hidden units. For networks without hidden units using the standard quadratic error function and a sigmoidal transfer function, we find that the error decreases as 1/t for large t, and the output states approach their target values as 1/√t. It is possible to obtain a different convergence rate for certain error and transfer functions, but the convergence can never be faster than 1/t. These results are unaffected by a momentum term in the learning algorithm, but convergence can be substantially improved by an adaptive learning rate scheme.

For networks with hidden units, we generally expect the same rate of convergence to be obtained as in the single-layer case; however, under certain circumstances one can obtain a polynomial speed-up for non sigmoidal units, or a logarithmic speed-up for sigmoidal units. Our analytic results are confirmed by empirical measurements of the convergence rate in numerical simulations.
Learning by Assertion: Two Methods for Calibrating a Linear Visual System
Authors: Maloney Laurence T. , Ahumada Albert J.
Published: March 2008

A visual system is geometrically calibrated if its estimates of the spatial properties of a scene are accurate: straight lines are judged straight, angles are correctly estimated, and collinear line segments are perceived to fall on a common line. This paper describes two new calibration methods for a model visual system whose photoreceptors are initially at unknown locations. The methods can also compensate for optical distortions that are equivalent to remapping of receptor locations (e.g., spherical aberration). The methods work by comparing visual input across eye/head movements; they require no explicit feedback and no knowledge about the particular contents of a scene. This work has implications for development and calibration in biological visual systems.
How to Generate Ordered Maps by Maximizing the Mutual Information between Input and Output Signals
Authors: Linsker Ralph
Published: March 2008

A learning rule that performs gradient ascent in the average mutual information between input and an output signal is derived for a system having feedforward and lateral interactions. Several processes emerge as components of this learning rule: Hebb-like modification, and cooperation and competition among processing nodes.

Topographic map formation is demonstrated using the learning rule. An analytic expression relating the average mutual information to the response properties of nodes and their geometric arrangement is derived in certain cases. This yields a relation between the local map magnification factor and the probability distribution in the input space. The results provide new links between unsupervised learning and information-theoretic optimization in a system whose properties are biologically motivated.
Finding Minimum Entropy Codes
Authors: Barlow H.B. , Kaushal T.P. , Mitchison G.J.
Published: March 2008

To determine whether a particular sensory event is a reliable predictor of reward or punishment it is necessary to know the prior probability of that event. If the variables of a sensory representation normally occur independently of each other, then it is possible to derive the prior probability of any logical function of the variables from the prior probabilities of the individual variables, without any additional knowledge; hence such a representation enormously enlarges the scope of definable events that can be searched for reliable predictors. Finding a Minimum Entropy Code is a possible method of forming such a representation, and methods for doing this are explored in this paper. The main results are (1) to show how to find such a code when the probabilities of the input states form a geometric progression, as is shown to be nearly true for keyboard characters in normal text; (2) to show how a Minimum Entropy Code can be approximated by repeatedly recoding pairs, triples, etc. of an original 7-bit code for keyboard characters; (3) to prove that in some cases enlarging the capacity of the output channel can lower the entropy.
How Cortical Interconnectedness Varies with Network Size
Authors: Stevens Charles F.
Published: March 2008

When model neural networks are used to gain insight into how the brain might carry out its computations, comparisons between features of the network and those of the brain form an important basis for drawing conclusions about the network's relevance to brain function. The most significant features to be compared, of course, relate to behavior of the units. Another network property that would be useful to consider, however, is the extent to which units are interconnected and the law by which unit-unit connections scale as the network is made larger. The goal of this paper is to consider these questions for neocortex. The conclusion will be that neocortical neurons are rather sparsely interconnected — each neuron receives direct synaptic input from fewer than 3% of its neighbors underlying the surrounding square millimeter of cortex — and the extent of connectedness hardly changes for brains that range in size over about four orders of magnitude. These conclusions support the currently popular notion that the brain's circuits are highly modular and suggest that increased cortex size is mainly achieved by adding more modules.
Random Neural Networks with Negative and Positive Signals and Product Form Solution
Authors: Gelenbe Erol
Published: March 2008

We introduce a new class of random “neural” networks in which signals are either negative or positive. A positive signal arriving at a neuron increases its total signal count or potential by one; a negative signal reduces it by one if the potential is positive, and has no effect if it is zero. When its potential is positive, a neuron “fires,” sending positive or negative signals at random intervals to neurons or to the outside. Positive signals represent excitatory signals and negative signals represent inhibition. We show that this model, with exponential signal emission intervals, Poisson external signal arrivals, and Markovian signal movements between neurons, has a product form leading to simple analytical expressions for the system state.
Backpropagation Applied to Handwritten Zip Code Recognition
Authors: LeCun Y. , Boser B. , Denker J. S. , Henderson D. , Howard R. E. , Hubbard W. , Jackel L. D.
Published: March 2008

The ability of learning networks to generalize can be greatly enhanced by providing constraints from the task domain. This paper demonstrates how such constraints can be integrated into a backpropagation network through the architecture of the network. This approach has been successfully applied to the recognition of handwritten zip code digits provided by the U.S. Postal Service. A single network learns the entire recognition operation, going from the normalized image of the character to the final classification.
A Subgrouping Strategy that Reduces Complexity and Speeds Up Learning in Recurrent Networks
Authors: Zipser David
Published: March 2008

An algorithm, called RTRL, for training fully recurrent neural networks has recently been studied by Williams and Zipser (1989a, b). Whereas RTRL has been shown to have great power and generality, it has the disadvantage of requiring a great deal of computation time. A technique is described here for reducing the amount of computation required by RTRL without changing the connectivity of the networks. This is accomplished by dividing the original network into subnets for the purpose of error propagation while leaving them undivided for activity propagation. An example is given of a 12-unit network that learns to be the finite-state part of a Turing machine and runs 10 times faster using the subgrouping strategy than the original algorithm.
Gram—Schmidt Neural Nets
Authors: Orfanidis Sophocles J.
Published: March 2008

A new type of feedforward multilayer neural net is proposed that exhibits fast convergence properties. It is defined by inserting a fast adaptive Gram-Schmidt preprocessor at each layer, followed by a conventional linear combiner-sigmoid part which is adapted by a fast version of the backpropagation rule. The resulting network structure is the multilayer generalization of the gradient adaptive lattice filter and the Gram-Schmidt adaptive array.
An Optoelectronic Architecture for Multilayer Learning in a Single Photorefractive Crystal
Authors: Peterson Carsten , Redfield Stephen , Keeler James D. , Hartman Eric
Published: March 2008

We propose a simple architecture for implementing supervised neural network models optically with photorefractive technology. The architecture is very versatile: a wide range of supervised learning algorithms can be implemented including mean-field-theory, backpropagation, and Kanerva-style networks. Our architecture is based on a single crystal with spatial multiplexing rather than the more commonly used angular multiplexing. It handles hidden units and places no restrictions on connectivity. Associated with spatial multiplexing are certain physical phenomena, rescattering and beam depletion, which tend to degrade the matrix multiplications. Detailed simulations including beam absorption and grating decay show that the supervised learning algorithms (slightly modified) compensate for these degradations.
Hebb-Type Dynamics is Sufficient to Account for the Inverse Magnification Rule in Cortical Somatotopy
Authors: Grajski Kamil A. , Merzenich Michael M.
Published: March 2008

The inverse magnification rule in cortical somatotopy is the experimentally derived inverse relationship between cortical magnification (area of somatotopic map representing a unit area of skin surface) and receptive field size (area of restricted skin surface driving a cortical neuron). We show by computer simulation of a simple, multilayer model that Hebb-type synaptic modification subject to competitive constraints is sufficient to account for the inverse magnification rule.
Optimal Plasticity from Matrix Memories: What Goes Up Must Come Down
Authors: Willshaw David , Dayan Peter
Published: March 2008

A recent article (Stanton and Sejnowski 1989) on long-term synaptic depression in the hippocampus has reopened the issue of the computational efficiency of particular synaptic learning rules (Hebb 1949; Palm 1988a; Morris and Willshaw 1989) — homosynaptic versus heterosynaptic and monotonic versus nonmonotonic changes in synaptic efficacy. We have addressed these questions by calculating and maximizing the signal-to-noise ratio, a measure of the potential fidelity of recall, in a class of associative matrix memories. Up to a multiplicative constant, there are three optimal rules, each providing for synaptic depression such that positive and negative changes in synaptic efficacy balance out. For one rule, which is found to be the Stent-Singer rule (Stent 1973; Rauschecker and Singer 1979), the depression is purely heterosynaptic; for another (Stanton and Sejnowski 1989), the depression is purely homosynaptic; for the third, which is a generalization of the first two, and has a higher signal-to-noise ratio, it is both heterosynaptic and homosynaptic. The third rule takes the form of a covariance rule (Sejnowski 1977a,b) and includes, as a special case, the prescription due to Hopfield (1982) and others (Willshaw 1971; Kohonen 1972).
Visual Perception of Three-Dimensional Motion
Authors: Heeger David J. , Jepson Allan
Published: March 2008

As an observer moves and explores the environment, the visual stimulation in his eye is constantly changing. Somehow he is able to perceive the spatial layout of the scene, and to discern his movement through space. Computational vision researchers have been trying to solve this problem for a number of years with only limited success. It is a difficult problem to solve because the relationship between the optical-flow field, the 3D motion parameters, and depth is nonlinear. We have come to understand that this nonlinear equation describing the optical-flow field can be split by an exact algebraic manipulation to yield an equation that relates the image velocities to the translational component of the 3D motion alone. Thus, the depth and the rotational velocity need not be known or estimated prior to solving for the translational velocity. The algorithm applies to the general case of arbitrary motion with respect to an arbitrary scene. It is simple to compute and it is plausible biologically.
Distributed Symbolic Representation of Visual Shape
Authors: Saund Eric
Published: March 2008

The notion of distributed representation has gained significance in explanations of connectionist or “neural” networks. This communication shows that the concept also offers motivation in devising representations for visual shape within a symbolic computing paradigm. In a representation for binary (silhouette) shapes, and in analogy with conventional distributed connectionist networks, descriptive power is gained when microfeatures are available naming important spatial relationships in images. Our symbolic approach is introduced through a vocabulary of 31 “hand built” shape descriptors operating in the two-dimensional shape world of the dorsal fins of fishes.
Analysis of Neural Networks with Redundancy
Authors: Izui Yoshio , Pentland Alex
Published: March 2008

Biological systems have a large degree of redundancy, a fact that is usually thought to have little effect beyond providing reliable function despite the death of individual neurons. We have discovered, however, that redundancy can qualitatively change the computations carried out by a network. We prove that for both feedforward and feedback networks the simple duplication of nodes and connections results in more accurate, faster, and more stable computation.
Stability of the Random Neural Network Model
Authors: Gelenbe Erol
Published: March 2008

In a recent paper (Gelenbe 1989) we introduced a new neural network model, called the Random Network, in which “negative” or “positive” signals circulate, modeling inhibitory and excitatory signals. These signals can arrive either from other neurons or from the outside world: they are summed at the input of each neuron and constitute its signal potential. The state of each neuron in this model is its signal potential, while the network state is the vector of signal potentials at each neuron. If its potential is positive, a neuron fires, and sends out signals to the other neurons of the network or to the outside world. As it does so its signal potential is depleted. We have shown (Gelenbe 1989) that in the Markovian case, this model has product form, that is, the steady-state probability distribution of its potential vector is the product of the marginal probabilities of the potential at each neuron. The signal flow equations of the network, which describe the rate at which positive or negative signals arrive at each neuron, are nonlinear, so that their existence and uniqueness are not easily established except for the case of feedforward (or backpropagation) networks (Gelenbe 1989). In this paper we show that whenever the solution to these signal flow equations exists, it is unique. We then examine two subclasses of networks — balanced and damped networks — and obtain stability conditions in each case. In practical terms, these stability conditions guarantee that the unique solution can be found to the signal flow equations and therefore that the network has a well-defined steady-state behavior.