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Journal articles on the topic 'Neural forms'

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Author: Grafiati

Published: 10 December 2022

Last updated: 29 January 2023

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1

Di Cesare, Giuseppe, Marzio Gerbella, and Giacomo Rizzolatti. "The neural bases of vitality forms." National Science Review 7, no. 1 (January 1, 2020): 202–13. http://dx.doi.org/10.1093/nsr/nwz187.

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Abstract Unlike emotions, which are short-lasting events accompanied by viscero-motor responses, vitality forms are continuous internal states that modulate the motor behaviors of individuals and are devoid of the autonomic modifications that characterize real emotions. Despite the importance of vitality forms in social life, only recently have neurophysiological studies been devoted to this issue. The first part of this review describes fMRI experiments, showing that the dorso-central insula is activated during the execution, the perception and the imagination of arm actions endowed with different vitality forms as well as during the hearing and the production of speech conveying vitality forms. In the second part, we address the means by which the dorso-central insula modulates the networks for controlling action execution and how the sensory and interoceptive information is conveyed to this insular sector. Finally, we present behavioral data showing the importance of vitality forms in social interactions.

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Ibarra, Oscar H., Andrei Păun, Gheorghe Păun, Alfonso Rodríguez-Patón, Petr Sosík, and Sara Woodworth. "Normal forms for spiking neural P systems." Theoretical Computer Science 372, no. 2-3 (March 2007): 196–217. http://dx.doi.org/10.1016/j.tcs.2006.11.025.

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3

Grossberg, Stephen. "Filling-in the forms." Behavioral and Brain Sciences 21, no. 6 (December 1998): 758–59. http://dx.doi.org/10.1017/s0140525x98341758.

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Boundary completion and surface filling-in are computationally complementary processes whose multiple processing stages form processing streams that realize a hierarchical resolution of uncertainty. Such complementarity and uncertainty principles provide a new foundation for philosophical discussions about visual perception, and lead to neural explanations of difficult perceptual data.

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Morgan, Peter, Bruce Curry, and Malcolm Beynon. "Comparing neural network approximations for different functional forms." Expert Systems 16, no. 2 (May 1999): 60–71. http://dx.doi.org/10.1111/1468-0394.00096.

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Lai, Tzu-Hsien, Ekaterina Protsenko, Yu-Chen Cheng, Marco L. Loggia, Gianluca Coppola, and Wei-Ta Chen. "Neural Plasticity in Common Forms of Chronic Headaches." Neural Plasticity 2015 (2015): 1–14. http://dx.doi.org/10.1155/2015/205985.

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Headaches are universal experiences and among the most common disorders. While headache may be physiological in the acute setting, it can become a pathological and persistent condition. The mechanisms underlying the transition from episodic to chronic pain have been the subject of intense study. Using physiological and imaging methods, researchers have identified a number of different forms of neural plasticity associated with migraine and other headaches, including peripheral and central sensitization, and alterations in the endogenous mechanisms of pain modulation. While these changes have been proposed to contribute to headache and pain chronification, some findings are likely the results of repetitive noxious stimulation, such as atrophy of brain areas involved in pain perception and modulation. In this review, we provide a narrative overview of recent advances on the neuroimaging, electrophysiological and genetic aspects of neural plasticity associated with the most common forms of chronic headaches, including migraine, cluster headache, tension-type headache, and medication overuse headache.

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Michaelides, Panayotis G., Angelos T. Vouldis, and Efthymios G. Tsionas. "Globally flexible functional forms: The neural distance function." European Journal of Operational Research 206, no. 2 (October 2010): 456–69. http://dx.doi.org/10.1016/j.ejor.2010.02.013.

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Schneidereit, Toni, and Michael Breuß. "Collocation polynomial neural forms and domain fragmentation for solving initial value problems." Neural Computing and Applications 34, no. 9 (December 27, 2021): 7141–56. http://dx.doi.org/10.1007/s00521-021-06860-4.

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AbstractSeveral neural network approaches for solving differential equations employ trial solutions with a feedforward neural network. There are different means to incorporate the trial solution in the construction, for instance, one may include them directly in the cost function. Used within the corresponding neural network, the trial solutions define the so-called neural form. Such neural forms represent general, flexible tools by which one may solve various differential equations. In this article, we consider time-dependent initial value problems, which require to set up the neural form framework adequately. The neural forms presented up to now in the literature for such a setting can be considered as first-order polynomials. In this work, we propose to extend the polynomial order of the neural forms. The novel collocation-type construction includes several feedforward neural networks, one for each order. Additionally, we propose the fragmentation of the computational domain into subdomains. The neural forms are solved on each subdomain, whereas the interfacing grid points overlap in order to provide initial values over the whole fragmentation. We illustrate in experiments that the combination of collocation neural forms of higher order and the domain fragmentation allows to solve initial value problems over large domains with high accuracy and reliability.

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Buchelnikov, М. А., М. U. Sidorova, О. V. Spirenkova, and М. Е. Nikulina. "Use of artificial neural networks for recognizing cannel forms." Interexpo GEO-Siberia 4 (May 18, 2022): 148–51. http://dx.doi.org/10.33764/2618-981x-2022-4-148-151.

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The possibilities of using artificial neural networks to solve some hydrological problems are considered. The description of the algorithm of the artificial neural network for recognizing the types of river rifts is given.

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Sazonova, N. G., T. A. Makarenko, and A. N. Narkevich. "Predicting various forms of endometriosis using artificial neural networks." Siberian Journal of Clinical and Experimental Medicine 35, no. 4 (December 25, 2020): 143–49. http://dx.doi.org/10.29001/2073-8552-2020-35-4-143-149.

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Introduction. Endometriosis is a difficult-to-diagnose pathology due to the diversity of clinical manifestations and the lack of high-precision markers necessary for rapid noninvasive diagnosis and timely administration of pathogenetically justified treatment.The aim of this work was to develop a computer system that allows us to assess the probability of endometriosis with various localizations in women, based on artificial neural networks.Material and Methods. The neural network mathematical models were constructed and tested based on data from 110 patients with morphologically pre-confirmed endometriosis. Patients were divided into training and test samples. The models were built based on anamnestic data and results of proteomic and enzyme immunoassays in blood plasma samples.Results and Discussion. In the course of the study, four mathematical models of neural networks were constructed to predict the presence or absence of endometriosis in a woman and its localization if present. Based on these mathematical models, a computer system “Differential diagnosis of endometriosis” was developed. This system allowed to assess the probability and localization of endometriosis in a patient based on parameters obtained as a result of neural network training.Conclusion. The developed computer diagnostic system allowed predicting the presence of endometriosis and its localization with a probability over 80%, depending on the predicted localization, based on data about the patient and the results of her examination. This system may be used for differential diagnosis of endometriosis from other diseases of the female reproductive system, as well as for differential diagnosis of various endometriosis localizations.

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Cohen, J. R., and R. A. Poldrack. "The Neural Correlates of Multiple Forms of Self-Control." NeuroImage 47 (July 2009): S178. http://dx.doi.org/10.1016/s1053-8119(09)71941-x.

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Izumida, Masanori, Kenji Murakami, and Tsunehiro Aibara. "Analysis of neural network energy functions using standard forms." Systems and Computers in Japan 23, no. 8 (1992): 36–45. http://dx.doi.org/10.1002/scj.4690230804.

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Makukhin, Kirill, and Scott Bolland. "Dissociable Forms of Repetition Priming: A Computational Model." Neural Computation 26, no. 4 (April 2014): 712–38. http://dx.doi.org/10.1162/neco_a_00569.

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Nondeclarative memory and novelty processing in the brain is an actively studied field of neuroscience, and reducing neural activity with repetition of a stimulus (repetition suppression) is a commonly observed phenomenon. Recent findings of an opposite trend—specifically, rising activity for unfamiliar stimuli—question the generality of repetition suppression and stir debate over the underlying neural mechanisms. This letter introduces a theory and computational model that extend existing theories and suggests that both trends are, in principle, the rising and falling parts of an inverted U-shaped dependence of activity with respect to stimulus novelty that may naturally emerge in a neural network with Hebbian learning and lateral inhibition. We further demonstrate that the proposed model is sufficient for the simulation of dissociable forms of repetition priming using real-world stimuli. The results of our simulation also suggest that the novelty of stimuli used in neuroscientific research must be assessed in a particularly cautious way. The potential importance of the inverted-U in stimulus processing and its relationship to the acquisition of knowledge and competencies in humans is also discussed.

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Ritchie, J. Brendan, and Peter Carruthers. "Massive modularity is consistent with most forms of neural reuse." Behavioral and Brain Sciences 33, no. 4 (August 2010): 289–90. http://dx.doi.org/10.1017/s0140525x10001081.

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AbstractAnderson claims that the hypothesis of massive neural reuse is inconsistent with massive mental modularity. But much depends upon how each thesis is understood. We suggest that the thesis of massive modularity presented in Carruthers (2006) is consistent with the forms of neural reuse that are actually supported by the data cited, while being inconsistent with a stronger version of reuse that Anderson seems to support.

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Crick, Francis. "Neural networks and REM sleep." Bioscience Reports 8, no. 6 (December 1, 1988): 531–35. http://dx.doi.org/10.1007/bf01117331.

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Cheng, Jianpeng, Siva Reddy, Vijay Saraswat, and Mirella Lapata. "Learning an Executable Neural Semantic Parser." Computational Linguistics 45, no. 1 (March 2019): 59–94. http://dx.doi.org/10.1162/coli_a_00342.

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This article describes a neural semantic parser that maps natural language utterances onto logical forms that can be executed against a task-specific environment, such as a knowledge base or a database, to produce a response. The parser generates tree-structured logical forms with a transition-based approach, combining a generic tree-generation algorithm with domain-general grammar defined by the logical language. The generation process is modeled by structured recurrent neural networks, which provide a rich encoding of the sentential context and generation history for making predictions. To tackle mismatches between natural language and logical form tokens, various attention mechanisms are explored. Finally, we consider different training settings for the neural semantic parser, including fully supervised training where annotated logical forms are given, weakly supervised training where denotations are provided, and distant supervision where only unlabeled sentences and a knowledge base are available. Experiments across a wide range of data sets demonstrate the effectiveness of our parser.

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Williams, R. K., C. Goridis, and R. Akeson. "Individual neural cell types express immunologically distinct N-CAM forms." Journal of Cell Biology 101, no. 1 (July 1, 1985): 36–42. http://dx.doi.org/10.1083/jcb.101.1.36.

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The neural cell adhesion molecules, or N-CAMs, are a group of structurally and immunologically related glycoproteins found in vertebrate neural tissues. Adult brain N-CAMs have apparent molecular weights of 180,000, 140,000, and 120,000. In this article we identify, using monoclonal antibody (Mab) 3G6.41, an immunologically distinct adult rat N-CAM form and show that this form is selectively expressed by some clonal neural cell lines. Consecutive immunoprecipitation experiments indicate that rabbit anti-N-CAM can remove from solubilized cerebellar neuron primary cultures all 180,000- and 140,000-mol-wt N-CAM molecules that react with Mab 3G6.41. However Mab 3G6.41 cannot remove all N-CAM molecules that react with rabbit anti-N-CAM. Rabbit anti-N-CAM binds to and immunoprecipitates N-CAM forms from the rat neuronal cell lines B35, B65, and B104, the glial lines B12 and C6, and L6 myoblasts. Mab 3G6.41 does not bind to or immunoprecipitate N-CAM from the B12 and B65 lines but does react with the other four lines by both criteria. Many cells in primary cultures of postnatal rat that express glial fibrillary acidic protein also bind Mab 3G6.41. Thus a unique form of rat N-CAM recognized by Mab 3G6.41 is found on some but not all neuronal, glial, and muscle cells.

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Guo, Z. X., J. Y. Yang, M. W. Dunlop, J. B. Cao, L. Y. Li, Y. D. Ma, K. F. Ji, C. Xiong, J. Li, and W. T. Ding. "Automatic classification of mesoscale auroral forms using convolutional neural networks." Journal of Atmospheric and Solar-Terrestrial Physics 235 (September 2022): 105906. http://dx.doi.org/10.1016/j.jastp.2022.105906.

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Ghosh, Mridul, Sk Md Obaidullah, Francesco Gherardini, and Maria Zdimalova. "Classification of Geometric Forms in Mosaics Using Deep Neural Network." Journal of Imaging 7, no. 8 (August 18, 2021): 149. http://dx.doi.org/10.3390/jimaging7080149.

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The paper addresses an image processing problem in the field of fine arts. In particular, a deep learning-based technique to classify geometric forms of artworks, such as paintings and mosaics, is presented. We proposed and tested a convolutional neural network (CNN)-based framework that autonomously quantifies the feature map and classifies it. Convolution, pooling and dense layers are three distinct categories of levels that generate attributes from the dataset images by introducing certain specified filters. As a case study, a Roman mosaic is considered, which is digitally reconstructed by close-range photogrammetry based on standard photos. During the digital transformation from a 2D perspective view of the mosaic into an orthophoto, each photo is rectified (i.e., it is an orthogonal projection of the real photo on the plane of the mosaic). Image samples of the geometric forms, e.g., triangles, squares, circles, octagons and leaves, even if they are partially deformed, were extracted from both the original and the rectified photos and originated the dataset for testing the CNN-based approach. The proposed method has proved to be robust enough to analyze the mosaic geometric forms, with an accuracy higher than 97%. Furthermore, the performance of the proposed method was compared with standard deep learning frameworks. Due to the promising results, this method can be applied to many other pattern identification problems related to artworks.

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Tao Song, Linqiang Pan, Jun Wang, I. Venkat, K. G. Subramanian, and R. Abdullah. "Normal Forms of Spiking Neural P Systems With Anti-Spikes." IEEE Transactions on NanoBioscience 11, no. 4 (December 2012): 352–59. http://dx.doi.org/10.1109/tnb.2012.2208122.

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Verfaellie, Mieke, and M. Keane. "The Neural Basis of Aware and Unaware Forms of Memory." Seminars in Neurology 17, no. 02 (June 1997): 153–61. http://dx.doi.org/10.1055/s-2008-1040925.

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21

Oliveri, Massimiliano, Paolo Maria Rossini, Maria Maddalena Filippi, Raimondo Traversa, Paola Cicinelli, and Carlo Caltagirone. "Specific forms of neural activity associated with tactile space awareness." Neuroreport 13, no. 8 (June 2002): 997–1001. http://dx.doi.org/10.1097/00001756-200206120-00002.

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22

Van, G. V., and I. A. Nekrasov. "Algorithm for ECG forms clustering based on artificial neural networks." Journal of Physics: Conference Series 1135 (December 2018): 012014. http://dx.doi.org/10.1088/1742-6596/1135/1/012014.

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23

Seller, Mary J. "Neural tube defects: Are neurulation and canalization forms causally distinct?" American Journal of Medical Genetics 35, no. 3 (March 1990): 394–96. http://dx.doi.org/10.1002/ajmg.1320350316.

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Pokorny, Christoph, Matias J. Ison, Arjun Rao, Robert Legenstein, Christos Papadimitriou, and Wolfgang Maass. "STDP Forms Associations between Memory Traces in Networks of Spiking Neurons." Cerebral Cortex 30, no. 3 (August 12, 2019): 952–68. http://dx.doi.org/10.1093/cercor/bhz140.

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Abstract Memory traces and associations between them are fundamental for cognitive brain function. Neuron recordings suggest that distributed assemblies of neurons in the brain serve as memory traces for spatial information, real-world items, and concepts. However, there is conflicting evidence regarding neural codes for associated memory traces. Some studies suggest the emergence of overlaps between assemblies during an association, while others suggest that the assemblies themselves remain largely unchanged and new assemblies emerge as neural codes for associated memory items. Here we study the emergence of neural codes for associated memory items in a generic computational model of recurrent networks of spiking neurons with a data-constrained rule for spike-timing-dependent plasticity. The model depends critically on 2 parameters, which control the excitability of neurons and the scale of initial synaptic weights. By modifying these 2 parameters, the model can reproduce both experimental data from the human brain on the fast formation of associations through emergent overlaps between assemblies, and rodent data where new neurons are recruited to encode the associated memories. Hence, our findings suggest that the brain can use both of these 2 neural codes for associations, and dynamically switch between them during consolidation.

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Kerosuo, Laura, Pushpa Neppala, Jenny Hsin, Sofie Mohlin, Felipe Monteleone Vieceli, Zsofia Török, Anni Laine, Jukka Westermarck, and Marianne E. Bronner. "Enhanced expression of MycN/CIP2A drives neural crest toward a neural stem cell-like fate: Implications for priming of neuroblastoma." Proceedings of the National Academy of Sciences 115, no. 31 (July 18, 2018): E7351—E7360. http://dx.doi.org/10.1073/pnas.1800039115.

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Neuroblastoma is a neural crest-derived childhood tumor of the peripheral nervous system in which MycN amplification is a hallmark of poor prognosis. Here we show that MycN is expressed together with phosphorylation-stabilizing factor CIP2A in regions of the neural plate destined to form the CNS, but MycN is excluded from the neighboring neural crest stem cell domain. Interestingly, ectopic expression of MycN or CIP2A in the neural crest domain biases cells toward CNS-like neural stem cells that express Sox2. Consistent with this, some forms of neuroblastoma have been shown to share transcriptional resemblance with CNS neural stem cells. As high MycN/CIP2A levels correlate with poor prognosis, we posit that a MycN/CIP2A-mediated cell-fate bias may reflect a possible mechanism underlying early priming of some aggressive forms of neuroblastoma. In contrast to MycN, its paralogue cMyc is normally expressed in the neural crest stem cell domain and typically is associated with better overall survival in clinical neuroblastoma, perhaps reflecting a more “normal” neural crest-like state. These data suggest that priming for some forms of aggressive neuroblastoma may occur before neural crest emigration from the CNS and well before sympathoadrenal specification.

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Chang, Yi-Fang. "Biological Lattice Gauge Theory as Modeling of Quantum Neural Networks." Journal of Modeling and Optimization 10, no. 1 (June 30, 2018): 23. http://dx.doi.org/10.32732/jmo.2018.10.1.23.

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Based on quantum biology and biological gauge field theory, we propose the biological lattice gauge theory as modeling of quantum neural networks. This method applies completely the same lattice theory in quantum field, but, whose two anomaly problems may just describe the double helical structure of DNA and violated chiral symmetry in biology. Further, we discuss the model of Neural Networks (NN) and the quantum neutral networks, which are related with biological loop quantum theory. Finally, we research some possible developments on described methods of networks by the extensive graph theory and their new mathematical forms.

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Sumida, Stuart Shigeo. "Two different vertebral forms in the axial column of Labidosaurus (Captorhinomorpha: Captorhinidae)." Journal of Paleontology 61, no. 1 (January 1987): 155–67. http://dx.doi.org/10.1017/s0022336000028304.

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The axial column in Labidosaurus is thoroughly described. Two different vertebral “morphs” are found to occur in this genus. One type is that which is classically illustrated for Labidosaurus, while the other shows alternation in height and construction of the neural arches and neural spines. A brief description of the modifications of muscle attachments in alternating forms is outlined. Analysis shows that such a structural modification probably allowed (more) efficient dorsiflexion and lateral flexion of the vertebral column. The occurrence of alternation of neural spine and arch structure is probably a primitive characteristic, but becomes variously modified in different forms. The variability observed in the axial column suggests that Labidosaurus may have been sexually dimorphic, or that the genus may consist of two different species.

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Magee, Jeffrey C., and Christine Grienberger. "Synaptic Plasticity Forms and Functions." Annual Review of Neuroscience 43, no. 1 (July 8, 2020): 95–117. http://dx.doi.org/10.1146/annurev-neuro-090919-022842.

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Synaptic plasticity, the activity-dependent change in neuronal connection strength, has long been considered an important component of learning and memory. Computational and engineering work corroborate the power of learning through the directed adjustment of connection weights. Here we review the fundamental elements of four broadly categorized forms of synaptic plasticity and discuss their functional capabilities and limitations. Although standard, correlation-based, Hebbian synaptic plasticity has been the primary focus of neuroscientists for decades, it is inherently limited. Three-factor plasticity rules supplement Hebbian forms with neuromodulation and eligibility traces, while true supervised types go even further by adding objectives and instructive signals. Finally, a recently discovered hippocampal form of synaptic plasticity combines the above elements, while leaving behind the primary Hebbian requirement. We suggest that the effort to determine the neural basis of adaptive behavior could benefit from renewed experimental and theoretical investigation of more powerful directed types of synaptic plasticity.

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Saxena, Aarush. "An Introduction to Convolutional Neural Networks." International Journal for Research in Applied Science and Engineering Technology 10, no. 12 (December 31, 2022): 943–47. http://dx.doi.org/10.22214/ijraset.2022.47789.

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Abstract: The field of machine learning has taken a dramatic twist in re- cent times, with the rise of the Artificial Neural Network (ANN). These biologically inspired computational models can far exceed the per- performance of previous forms of artificial intelligence in common machine-learning tasks. One of the most impressive forms of ANN architecture is that of the Convolutional Neural Network (CNN). CNNs are primarily used to solve difficult image-driven pattern recognition tasks and with their precise yet simple architecture, offer a simplified method of getting started with ANNs. This document briefly introduces CNNs, discussing recently published papers and newly formed techniques in developing this bill- leniently fantastic image recognition models. This introduction assumes you are familiar with ANNs and machine learning fundamentals.

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Bartsev, S. I., P. M. Baturina, and G. M. Markova. "Neural Network-Based Decoding Input Stimulus Data Based on Recurrent Neural Network Neural Activity Pattern." Doklady Biological Sciences 502, no. 1 (March 17, 2022): 1–5. http://dx.doi.org/10.1134/s001249662201001x.

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Abstract The paper reports the assessment of the possibility to recover information obtained using an artificial neural network via inspecting neural activity patterns. A simple recurrent neural network forms dynamic excitation patterns for storing data on input stimulus in the course of the advanced delayed match to sample test with varying duration of pause between the received stimuli. Information stored in these patterns can be used by the neural network at any moment within the specified interval (three to six clock cycles), whereby it appears possible to detect invariant representation of received stimulus. To identify these representations, the neural network-based decoding method that shows 100% efficiency of received stimuli recognition has been suggested. This method allows for identification the minimum subset of neurons, the excitation pattern of which contains comprehensive information about the stimulus received by the neural network.

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Perrotta, Giulio. "Attention Deficit Hyperactivity Disorder: definition, contexts, neural correlates and clinical strategies." Archives of Medical Case Reports and Case Study. 2, no. 2 (October 14, 2019): 01–07. http://dx.doi.org/10.31579/2688-7517/011.

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Starting from the definition of "Attention Deficit and Hyperactivity Disorder" (ADHD), we proceeded to list the individual forms envisaged by the DSM-V, with a series of focus on clinical, neuropsychological and therapeutic profiles, concluding the analysis on the context resolution of the problems deriving from the disturbance in question

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Venticinque, Joseph S., Rajpreet Chahal, Sarah J. Beard, Roberta A. Schriber, Paul D. Hastings, and Amanda E. Guyer. "Neural responses to implicit forms of peer influence in young adults." Social Neuroscience 16, no. 3 (April 16, 2021): 327–40. http://dx.doi.org/10.1080/17470919.2021.1911843.

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Tao Song, Linqiang Pan, Keqin Jiang, Bosheng Song, and Wei Chen. "Normal Forms for Some Classes of Sequential Spiking Neural P Systems." IEEE Transactions on NanoBioscience 12, no. 3 (September 2013): 255–64. http://dx.doi.org/10.1109/tnb.2013.2271278.

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Lai, Josephine, Jolanta Jedrzejczyk, John A. Pizzey, David Green, and Eric A. Barnard. "Neural control of the forms of acetylcholinesterase in slow mammalian muscles." Nature 321, no. 6065 (May 1986): 72–74. http://dx.doi.org/10.1038/321072a0.

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Hall, J., H. C. Whalley, J. W. McKirdy, R. Sprengelmeyer, I. M. Santos, D. I. Donaldson, D. J. McGonigle, et al. "A common neural system mediating two different forms of social judgement." Psychological Medicine 40, no. 7 (October 8, 2009): 1183–92. http://dx.doi.org/10.1017/s0033291709991395.

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BackgroundA wide range of neuropsychiatric conditions, including schizophrenia and autistic spectrum disorder (ASD), are associated with impairments in social function. Previous studies have shown that individuals with schizophrenia and ASD have deficits in making a wide range of social judgements from faces, including decisions related to threat (such as judgements of approachability) and decisions not related to physical threat (such as judgements of intelligence). We have investigated healthy control participants to see whether there is a common neural system activated during such social decisions, on the basis that deficits in this system may contribute to the impairments seen in these disorders.MethodWe investigated the neural basis of social decision making during judgements of approachability and intelligence from faces in 24 healthy participants using functional magnetic resonance imaging (fMRI). We used conjunction analysis to identify common brain regions activated during both tasks.ResultsActivation of the amygdala, medial prefrontal cortex, inferior prefrontal cortex and cerebellum was seen during performance of both social tasks, compared to simple gender judgements from the same stimuli. Task-specific activations were present in the dorsolateral prefrontal cortex in the intelligence task and in the inferior and middle temporal cortex in the approachability task.ConclusionsThe present study identified a common network of brain regions activated during the performance of two different forms of social judgement from faces. Dysfunction of this network is likely to contribute to the broad-ranging deficits in social function seen in psychiatric disorders such as schizophrenia and ASD.

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Contreras, Juan Manuel, Mahzarin R. Banaji, and Jason P. Mitchell. "Dissociable neural correlates of stereotypes and other forms of semantic knowledge." Social Cognitive and Affective Neuroscience 7, no. 7 (September 9, 2011): 764–70. http://dx.doi.org/10.1093/scan/nsr053.

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Rasmussen, Rune, and Ubadah Sabbagh. "Neural Polyamory: One Cell Forms Meaningful Connections with Hundreds of Partners." Cell Systems 10, no. 5 (May 2020): 381–83. http://dx.doi.org/10.1016/j.cels.2020.04.009.

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Wagner, Anthony D., Anat Maril, and Daniel L. Schacter. "Interactions Between Forms of Memory: When Priming Hinders New Episodic Learning." Journal of Cognitive Neuroscience 12, supplement 2 (November 2000): 52–60. http://dx.doi.org/10.1162/089892900564064.

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Human memory consists of multiple forms, including priming and explicit memory. Although considerable evidence indicates that priming and explicit memory are functionally and neuroanatomically distinct, little is know about when and how these different forms of memory interact. Here, behavioral and functional magnetic resonance imaging (fMRI) methods were used to examine a novel and counterintuitive hypothesis: Priming during episodic encoding may be negatively associated with subsequent explicit memory. Using an experimental design that exploited known properties of spacing or lag effects, the magnitudes of behavioral and neural priming during a second study episode were varied and the relation between these magnitudes of priming during re-encoding and performance on a subsequent explicit memory test was examined. Results revealed that greater behavioral priming (reduced reaction times) and neural priming (reduced left inferior prefrontal brain activation) during re-encoding were associated with lower levels of subsequent explicit memory. Moreover, those subjects who demonstrated greater behavioral and neural priming effects during re-encoding following a long lag tended to demonstrate the least benefit in subsequent explicit memory due to this second study episode. These findings suggest that priming for past experiences can hinder new episodic encoding.

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Torkamani, MohamadAli, Shiv Shankar, Amirmohammad Rooshenas, and Phillip Wallis. "Differential Equation Units: Learning Functional Forms of Activation Functions from Data." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (April 3, 2020): 6030–37. http://dx.doi.org/10.1609/aaai.v34i04.6065.

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Most deep neural networks use simple, fixed activation functions, such as sigmoids or rectified linear units, regardless of domain or network structure. We introduce differential equation units (DEUs), an improvement to modern neural networks, which enables each neuron to learn a particular nonlinear activation function from a family of solutions to an ordinary differential equation. Specifically, each neuron may change its functional form during training based on the behavior of the other parts of the network. We show that using neurons with DEU activation functions results in a more compact network capable of achieving comparable, if not superior, performance when compared to much larger networks.

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Perrotta, G. "Parkinson’s disorder: definitions, contexts, neural correlates, strategies and clinical approaches." Neuroscience and Neurological Surgery 4, no. 5 (September 27, 2019): 01–07. http://dx.doi.org/10.31579/2578-8868/079.

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Starting from the definition analysis of Parkinson's disorder, we proceeded to study the clinical, psychological and socio-environmental context, to better analyze the intrinsic and extrinsic aspects of this chronic progressive neurodegenerative pathology, placing emphasis also on secondary forms. The present contribution focuses in particular on all the most significant elements, also from an etiological and neurobiological point of view, in order to present the best therapies and treatments known today in medical and neuropsychotherapeutic profiles.

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Lu, Jun, and Hiroshi Ohta. "A Powerful Neural Network Method with Digital-contract Hints for Pricing Complex Options." Journal of Advanced Computational Intelligence and Intelligent Informatics 7, no. 2 (June 20, 2003): 139–46. http://dx.doi.org/10.20965/jaciii.2003.p0139.

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Many researches have proved that common neural network methods outperform parametric methods for option pricing. However, performance of the common neural network method usually suffers from the non-stationary and noisy properties of observed financial data. In this paper, we propose some parametric digital-contract (DC) hints, which can be utilized as auxiliary information to guide a neural network’s learning process about target pricing formula, and thus can be expected to get a better pricing performance in the case of observed data with noise. The DC hints are incorporated into a neural network with serial and parallel forms. Some Monte Carlo simulation experiments are performed and demonstrated that both the two forms not only have the nonparametric method’s advantages like generalization and superior accuracy, but also have the parametric method’s robust property to financial data with noise. The results also show that these two forms have their own strengths and limitations.

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Tang, Joey, Jamie Ward, and Brian Butterworth. "Number Forms in the Brain." Journal of Cognitive Neuroscience 20, no. 9 (September 2008): 1547–56. http://dx.doi.org/10.1162/jocn.2008.20120.

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Mental images of number lines, Galton's “number forms” (NF), are a useful way of investigating the relation between number and space. Here we report the first neuroimaging study of number-form synesthesia, investigating 10 synesthetes with NFs going from left to right compared with matched controls. Neuroimaging with functional magnetic resonance imaging revealed no difference in brain activation during a task focused on number magnitude but, in a comparable task on number order, synesthetes showed additional activations in the left and right posterior intraparietal sulci, suggesting that NFs are essentially ordinal in nature. Our results suggest that there are separate but partially overlapping neural circuits for the processing of ordinal and cardinal numbers, irrespective of the presence of an NF, but a core region in the anterior intraparietal sulcus representing (cardinal) number meaning appears to be activated autonomously, irrespective of task. This article provides an important extension beyond previous studies that have focused on word-color or grapheme-color synesthesia.

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Ibrić, Svetlana, Jelena Djuriš, Jelena Parojčić, and Zorica Djurić. "Artificial Neural Networks in Evaluation and Optimization of Modified Release Solid Dosage Forms." Pharmaceutics 4, no. 4 (October 18, 2012): 531–50. http://dx.doi.org/10.3390/pharmaceutics4040531.

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Implementation of the Quality by Design (QbD) approach in pharmaceutical development has compelled researchers in the pharmaceutical industry to employ Design of Experiments (DoE) as a statistical tool, in product development. Among all DoE techniques, response surface methodology (RSM) is the one most frequently used. Progress of computer science has had an impact on pharmaceutical development as well. Simultaneous with the implementation of statistical methods, machine learning tools took an important place in drug formulation. Twenty years ago, the first papers describing application of artificial neural networks in optimization of modified release products appeared. Since then, a lot of work has been done towards implementation of new techniques, especially Artificial Neural Networks (ANN) in modeling of production, drug release and drug stability of modified release solid dosage forms. The aim of this paper is to review artificial neural networks in evaluation and optimization of modified release solid dosage forms.

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Thompson, Jessica A. F. "Forms of explanation and understanding for neuroscience and artificial intelligence." Journal of Neurophysiology 126, no. 6 (December 1, 2021): 1860–74. http://dx.doi.org/10.1152/jn.00195.2021.

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Much of the controversy evoked by the use of deep neural networks as models of biological neural systems amount to debates over what constitutes scientific progress in neuroscience. To discuss what constitutes scientific progress, one must have a goal in mind (progress toward what?). One such long-term goal is to produce scientific explanations of intelligent capacities (e.g., object recognition, relational reasoning). I argue that the most pressing philosophical questions at the intersection of neuroscience and artificial intelligence are ultimately concerned with defining the phenomena to be explained and with what constitute valid explanations of such phenomena. I propose that a foundation in the philosophy of scientific explanation and understanding can scaffold future discussions about how an integrated science of intelligence might progress. Toward this vision, I review relevant theories of scientific explanation and discuss strategies for unifying the scientific goals of neuroscience and AI.

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Race, Elizabeth A., Shanti Shanker, and Anthony D. Wagner. "Neural Priming in Human Frontal Cortex: Multiple Forms of Learning Reduce Demands on the Prefrontal Executive System." Journal of Cognitive Neuroscience 21, no. 9 (September 2009): 1766–81. http://dx.doi.org/10.1162/jocn.2009.21132.

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Past experience is hypothesized to reduce computational demands in PFC by providing bottom–up predictive information that informs subsequent stimulus-action mapping. The present fMRI study measured cortical activity reductions (“neural priming”/“repetition suppression”) during repeated stimulus classification to investigate the mechanisms through which learning from the past decreases demands on the prefrontal executive system. Manipulation of learning at three levels of representation—stimulus, decision, and response—revealed dissociable neural priming effects in distinct frontotemporal regions, supporting a multiprocess model of neural priming. Critically, three distinct patterns of neural priming were identified in lateral frontal cortex, indicating that frontal computational demands are reduced by three forms of learning: (a) cortical tuning of stimulus-specific representations, (b) retrieval of learned stimulus-decision mappings, and (c) retrieval of learned stimulus-response mappings. The topographic distribution of these neural priming effects suggests a rostrocaudal organization of executive function in lateral frontal cortex.

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I, Ankith. "Convolutional Neural Network for Image Recognition." International Journal for Research in Applied Science and Engineering Technology 9, no. 11 (November 30, 2021): 1619–24. http://dx.doi.org/10.22214/ijraset.2021.39061.

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Abstract: Recent developments in the field of machine learning have changed the way it operates for ever, especially with the rise of Artificial Neural Networks (ANN). There is no doubt that these biologically inspired computational models are capable of performing far better than previous forms of artificial intelligence in common machine learning tasks as compared to their previous versions. There are several different forms of artificial neural networks (ANNs), but one of the most impressive is the convolutional neural network (CNN). CNN's have been extensively used for solving difficult pattern recognition tasks using images. With their simple yet precise architecture, they offer a simplified approach to getting started with ANNs. The goal of this paper is to provide a brief introduction to CNN. It discusses the latest papers and newly formed techniques in order to develop these absolutely brilliant models of image recognition. This introduction assumes that you already have a basic understanding of ANNs and machine learning. Keywords: Pattern recognition, artificial neural networks, machine learning, image analysing.

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Perrotta, Giulio. "Behavioral Addiction Disorder: Definition, Classifications, Clinical Contexts, Neural Correlates and Clinical Strategies." Addiction Research and Adolescent Behaviour 2, no. 2 (October 18, 2019): 01–10. http://dx.doi.org/10.31579/2688-7517/010.

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Starting from the categorical definition of " behavioral addiction disorder", we proceeded to list the individual forms provided by the DSM-V, with a particular focus on historical, clinical, neurobiological and therapeutic profiles, concluding the analysis of the possible strategies to be used to finalize the resolutions to problems arising from the disorder in question.

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Williams, Christopher K. I. "Computation with Infinite Neural Networks." Neural Computation 10, no. 5 (July 1, 1998): 1203–16. http://dx.doi.org/10.1162/089976698300017412.

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For neural networks with a wide class of weight priors, it can be shown that in the limit of an infinite number of hidden units, the prior over functions tends to a gaussian process. In this article, analytic forms are derived for the covariance function of the gaussian processes corresponding to networks with sigmoidal and gaussian hidden units. This allows predictions to be made efficiently using networks with an infinite number of hidden units and shows, somewhat paradoxically, that it may be easier to carry out Bayesian prediction with infinite networks rather than finite ones.

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Alharthi, Adil. "Convolutional Neural Network based on Transfer Learning for Medical Forms Classification 34." International Journal of Advanced Trends in Computer Science and Engineering 8, no. 6 (December 15, 2019): 3405–11. http://dx.doi.org/10.30534/ijatcse/2019/115862019.

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Farrag, Mahmoud, and Nic Leipzig. "Subcutaneous Maturation of Neural Stem Cell-Loaded Hydrogels Forms Region-Specific Neuroepithelium." Cells 7, no. 10 (October 17, 2018): 173. http://dx.doi.org/10.3390/cells7100173.

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A combinatorial approach integrating stem cells and capable of exploiting available cues is likely needed to regenerate lost neural tissues and ultimately restore neurologic functions. This study investigates the effects of the subcutaneous maturation of adult-derived neural stem cell (aNSCs) seeded into biomaterial constructs on aNSC differentiation and ultimate regional neuronal identity as a first step toward a future spinal cord injury treatment. To achieve this, we encapsulated rat aNSCs in chitosan-based hydrogels functionalized with immobilized azide-tagged interferon-γ inside a chitosan conduit. Then, we implanted these constructs in the subcutaneous tissues in the backs of rats in the cervical, thoracic, and lumbar regions for 4, 6, and 8 weeks. After harvesting the scaffolds, we analyzed cell differentiation qualitatively using immunohistochemical analysis and quantitatively using RT-qPCR. Results revealed that the hydrogels supported aNSC survival and differentiation up to 4 weeks in the subcutaneous environment as marked by the expression of several neurogenesis markers. Most interesting, the aNSCs expressed region-specific Hox genes corresponding to their region of implantation. This study lays the groundwork for further translational work to recapitulate the potentially undiscovered patterning cues in the subcutaneous tissue and provide support for the conceptual premise that our bioengineering approach can form caudalized region-specific neuroepithelium.

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