Gotowe bibliografie tematyczne / NEURONS NEURAL NETWORK

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  1. Artykuły w czasopismach
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Gotowa bibliografia na temat „NEURONS NEURAL NETWORK”

Autor: Grafiati
Data publikacji: 11 września 2023

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Artykuły w czasopismach na temat "NEURONS NEURAL NETWORK"

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Ribar, Srdjan, Vojislav V. Mitic, and Goran Lazovic. "Neural Networks Application on Human Skin Biophysical Impedance Characterizations." Biophysical Reviews and Letters 16, no. 01 (2021): 9–19. http://dx.doi.org/10.1142/s1793048021500028.

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Artificial neural networks (ANNs) are basically the structures that perform input–output mapping. This mapping mimics the signal processing in biological neural networks. The basic element of biological neural network is a neuron. Neurons receive input signals from other neurons or the environment, process them, and generate their output which represents the input to another neuron of the network. Neurons can change their sensitivity to input signals. Each neuron has a simple rule to process an input signal. Biological neural networks have the property that signals are processed through many p
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Dalhoum, Abdel Latif Abu, and Mohammed Al-Rawi. "High-Order Neural Networks are Equivalent to Ordinary Neural Networks." Modern Applied Science 13, no. 2 (2019): 228. http://dx.doi.org/10.5539/mas.v13n2p228.

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Equivalence of computational systems can assist in obtaining abstract systems, and thus enable better understanding of issues related their design and performance. For more than four decades, artificial neural networks have been used in many scientific applications to solve classification problems as well as other problems. Since the time of their introduction, multilayer feedforward neural network referred as Ordinary Neural Network (ONN), that contains only summation activation (Sigma) neurons, and multilayer feedforward High-order Neural Network (HONN), that contains Sigma neurons, and prod
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Geva, Shlomo, and Joaquin Sitte. "An Exponential Response Neural Net." Neural Computation 3, no. 4 (1991): 623–32. http://dx.doi.org/10.1162/neco.1991.3.4.623.

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By using artificial neurons with exponential transfer functions one can design perfect autoassociative and heteroassociative memory networks, with virtually unlimited storage capacity, for real or binary valued input and output. The autoassociative network has two layers: input and memory, with feedback between the two. The exponential response neurons are in the memory layer. By adding an encoding layer of conventional neurons the network becomes a heteroassociator and classifier. Because for real valued input vectors the dot-product with the weight vector is no longer a measure for similarit
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Sharma, Subhash Kumar. "An Overview on Neural Network and Its Application." International Journal for Research in Applied Science and Engineering Technology 9, no. 8 (2021): 1242–48. http://dx.doi.org/10.22214/ijraset.2021.37597.

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Abstract: In this paper an overview on neural network and its application is focused. In Real-world business applications for neural networks are booming. In some cases, NNs have already become the method of choice for businesses that use hedge fund analytics, marketing segmentation, and fraud detection. Here are some neural network innovators who are changing the business landscape. Here shown that how the biological model of neural network functions, all mammalian brains consist of interconnected neurons that transmit electrochemical signals. Neurons have several components: the body, which
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YAMAZAKI, TADASHI, and SHIGERU TANAKA. "A NEURAL NETWORK MODEL FOR TRACE CONDITIONING." International Journal of Neural Systems 15, no. 01n02 (2005): 23–30. http://dx.doi.org/10.1142/s0129065705000037.

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We studied the dynamics of a neural network that has both recurrent excitatory and random inhibitory connections. Neurons started to become active when a relatively weak transient excitatory signal was presented and the activity was sustained due to the recurrent excitatory connections. The sustained activity stopped when a strong transient signal was presented or when neurons were disinhibited. The random inhibitory connections modulated the activity patterns of neurons so that the patterns evolved without recurrence with time. Hence, a time passage between the onsets of the two transient sig
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Kim, Choongmin, Jacob A. Abraham, Woochul Kang, and Jaeyong Chung. "A Neural Network Decomposition Algorithm for Mapping on Crossbar-Based Computing Systems." Electronics 9, no. 9 (2020): 1526. http://dx.doi.org/10.3390/electronics9091526.

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Crossbar-based neuromorphic computing to accelerate neural networks is a popular alternative to conventional von Neumann computing systems. It is also referred as processing-in-memory and in-situ analog computing. The crossbars have a fixed number of synapses per neuron and it is necessary to decompose neurons to map networks onto the crossbars. This paper proposes the k-spare decomposition algorithm that can trade off the predictive performance against the neuron usage during the mapping. The proposed algorithm performs a two-level hierarchical decomposition. In the first global decomposition
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Atanassov, Krassimir, Sotir Sotirov, and Tania Pencheva. "Intuitionistic Fuzzy Deep Neural Network." Mathematics 11, no. 3 (2023): 716. http://dx.doi.org/10.3390/math11030716.

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The concept of an intuitionistic fuzzy deep neural network (IFDNN) is introduced here as a demonstration of a combined use of artificial neural networks and intuitionistic fuzzy sets, aiming to benefit from the advantages of both methods. The investigation presents in a methodological way the whole process of IFDNN development, starting with the simplest form—an intuitionistic fuzzy neural network (IFNN) with one layer with single-input neuron, passing through IFNN with one layer with one multi-input neuron, further subsequent complication—an IFNN with one layer with many multi-input neurons,
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Bensimon, Moshe, Shlomo Greenberg, and Moshe Haiut. "Using a Low-Power Spiking Continuous Time Neuron (SCTN) for Sound Signal Processing." Sensors 21, no. 4 (2021): 1065. http://dx.doi.org/10.3390/s21041065.

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This work presents a new approach based on a spiking neural network for sound preprocessing and classification. The proposed approach is biologically inspired by the biological neuron’s characteristic using spiking neurons, and Spike-Timing-Dependent Plasticity (STDP)-based learning rule. We propose a biologically plausible sound classification framework that uses a Spiking Neural Network (SNN) for detecting the embedded frequencies contained within an acoustic signal. This work also demonstrates an efficient hardware implementation of the SNN network based on the low-power Spike Continuous Ti
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Weaver, Adam L., and Scott L. Hooper. "Follower Neurons in Lobster (Panulirus interruptus) Pyloric Network Regulate Pacemaker Period in Complementary Ways." Journal of Neurophysiology 89, no. 3 (2003): 1327–38. http://dx.doi.org/10.1152/jn.00704.2002.

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Distributed neural networks (ones characterized by high levels of interconnectivity among network neurons) are not well understood. Increased insight into these systems can be obtained by perturbing network activity so as to study the functions of specific neurons not only in the network's “baseline” activity but across a range of network activities. We applied this technique to study cycle period control in the rhythmic pyloric network of the lobster, Panulirus interruptus. Pyloric rhythmicity is driven by an endogenous oscillator, the Anterior Burster (AB) neuron. Two network neurons feed ba
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Sajedinia, Zahra, and Sébastien Hélie. "A New Computational Model for Astrocytes and Their Role in Biologically Realistic Neural Networks." Computational Intelligence and Neuroscience 2018 (July 5, 2018): 1–10. http://dx.doi.org/10.1155/2018/3689487.

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Recent studies in neuroscience show that astrocytes alongside neurons participate in modulating synapses. It led to the new concept of “tripartite synapse”, which means that a synapse consists of three parts: presynaptic neuron, postsynaptic neuron, and neighboring astrocytes. However, it is still unclear what role is played by the astrocytes in the tripartite synapse. Detailed biocomputational modeling may help generate testable hypotheses. In this article, we aim to study the role of astrocytes in synaptic plasticity by exploring whether tripartite synapses are capable of improving the perfo
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Rozprawy doktorskie na temat "NEURONS NEURAL NETWORK"

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Voysey, Matthew David. "Inexact analogue CMOS neurons for VLSI neural network design." Thesis, University of Southampton, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.264387.

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Lukashev, A. "Basics of artificial neural networks (ANNs)." Thesis, Київський національний університет технологій та дизайну, 2018. https://er.knutd.edu.ua/handle/123456789/11353.

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Schmidt, Peter H. (Peter Harrison). "The transfer characteristic of neurons in a pulse-code neural network." Thesis, Massachusetts Institute of Technology, 1988. http://hdl.handle.net/1721.1/14594.

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Brady, Patrick. "Internal representation and biological plausibility in an artificial neural network." Thesis, Brunel University, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.311273.

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Hunter, Russell I. "Improving associative memory in a network of spiking neurons." Thesis, University of Stirling, 2011. http://hdl.handle.net/1893/6177.

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In this thesis we use computational neural network models to examine the dynamics and functionality of the CA3 region of the mammalian hippocampus. The emphasis of the project is to investigate how the dynamic control structures provided by inhibitory circuitry and cellular modification may effect the CA3 region during the recall of previously stored information. The CA3 region is commonly thought to work as a recurrent auto-associative neural network due to the neurophysiological characteristics found, such as, recurrent collaterals, strong and sparse synapses from external inputs and plastic
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D'Alton, S. "A Constructive Neural Network Incorporating Competitive Learning of Locally Tuned Hidden Neurons." Thesis, Honours thesis, University of Tasmania, 2005. https://eprints.utas.edu.au/243/1/D%27Alton05CompetitivelyTrainedRAN.pdf.

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Performance metrics are a driving force in many fields of work today. The field of constructive neural networks is no different. In this field, the popular measurement metrics (resultant network size, test set accuracy) are difficult to maximise, given their dependence on several varied factors, of which the mostimportant is the dataset to be applied. This project set out with the intention to minimise the number of hidden units installed into a resource allocating network (RAN) (Platt 1991), whilst increasing the accuracy by means of application of competitive learning techniques. Three d
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Grehl, Stephanie. "Stimulation-specific effects of low intensity repetitive magnetic stimulation on cortical neurons and neural circuit repair in vitro (studying the impact of pulsed magnetic fields on neural tissue)." Thesis, Paris 6, 2014. http://www.theses.fr/2014PA066706/document.

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Les champs électromagnétiques sont couramment utilisés pour stimuler de manière non-invasive le cerveau humain soit à des fins thérapeutiques ou dans un contexte de recherche. Les effets de la stimulation magnétique varient en fonction de la fréquence et de l'intensité du champ magnétique. Les mécanismes mis en jeu restent inconnus, d'autant plus lors de stimulations à faible intensité. Dans cette thèse, nous avons évalué les effets de stimulations magnétiques répétées à différentes fréquences appliqués à faible intensité (10-13 mT ; Low Intensity Repetitive Magnetic Stimulation : LI-rMS) in v
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Gettner, Jonathan A. "Identifying and Predicting Rat Behavior Using Neural Networks." DigitalCommons@CalPoly, 2015. https://digitalcommons.calpoly.edu/theses/1513.

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The hippocampus is known to play a critical role in episodic memory function. Understanding the relation between electrophysiological activity in a rat hippocampus and rat behavior may be helpful in studying pathological diseases that corrupt electrical signaling in the hippocampus, such as Parkinson’s and Alzheimer’s. Additionally, having a method to interpret rat behaviors from neural activity may help in understanding the dynamics of rat neural activity that are associated with certain identified behaviors. In this thesis, neural networks are used as a black-box model to map electrophysiolo
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Vissani, Matteo. "Multisensory features of peripersonal space representation: an analysis via neural network modelling." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2017.

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The peripersonal space (PPS) is the space immediately surrounding the body. It is coded in the brain in a multisensory, body part-centered (e.g. hand-centered, trunk-centered), modular fashion. This is supported by the existence of multisensory neurons (in fronto-parietal areas) with tactile receptive field on a specific body part (hand, arm, trunk, etc.) and visual/auditory receptive field surrounding the same body part. Recent behavioural results (Serino et al. Sci Rep 2015), obtained by using an audio-tactile paradigm, have further supported the existence of distinct PPS representations, ea
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Yao, Yong. "A neural network in the pond snail, Planorbis corneus : electrophysiology and morphology of pleural ganglion neurons and their input neurons /." [S.l.] : [s.n.], 1986. http://www.ub.unibe.ch/content/bibliotheken_sammlungen/sondersammlungen/dissen_bestellformular/index_ger.html.

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Książki na temat "NEURONS NEURAL NETWORK"

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Gerstner, Wulfram. Spiking neuron models: Single neurons, populations, plasticity. Cambridge University Press, 2002.

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Eeckman, Frank H. Computation in neurons and neural systems. Springer, 1994.

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H, Eeckman Frank, and Conference on Computation and Neural Systems (1993 : Washington, D.C.), eds. Computation in neurons and neural systems. Kluwer Academic Publishers, 1994.

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Fellin, Tommaso, and Michael Halassa, eds. Neuronal Network Analysis. Humana Press, 2012. http://dx.doi.org/10.1007/978-1-61779-633-3.

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1931-, Taylor John Gerald, and Mannion C. L. T, eds. Coupled oscillating neurons. Springer-Verlag, 1992.

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Lek, Sovan, and Jean-François Guégan, eds. Artificial Neuronal Networks. Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/978-3-642-57030-8.

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Lek, Sovan. Artificial Neuronal Networks. Springer Berlin Heidelberg, 2000.

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Aizenberg, Igor. Complex-Valued Neural Networks with Multi-Valued Neurons. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-20353-4.

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service), SpringerLink (Online, ed. Complex-Valued Neural Networks with Multi-Valued Neurons. Springer Berlin Heidelberg, 2011.

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G, Stein Paul S., ed. Neurons, networks, and motor behavior. MIT Press, 1997.

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Części książek na temat "NEURONS NEURAL NETWORK"

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De Wilde, Philippe. "Neurons in the Brain." In Neural Network Models. Springer London, 1997. http://dx.doi.org/10.1007/978-1-84628-614-8_3.

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Marinaro, Maria. "Deterministic Networks with Ternary Neurons." In Neural Network Dynamics. Springer London, 1992. http://dx.doi.org/10.1007/978-1-4471-2001-8_5.

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Taylor, J. G. "Temporal Patterns and Leaky Integrator Neurons." In International Neural Network Conference. Springer Netherlands, 1990. http://dx.doi.org/10.1007/978-94-009-0643-3_144.

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Pawelzik, K., F. Wolf, J. Deppisch, and T. Geisel. "Network Dynamics and Correlated Spikes." In Computation in Neurons and Neural Systems. Springer US, 1994. http://dx.doi.org/10.1007/978-1-4615-2714-5_17.

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Bleich, M. E., and R. V. Jensen. "Fatigue in a Dynamic Neural Network." In Computation in Neurons and Neural Systems. Springer US, 1994. http://dx.doi.org/10.1007/978-1-4615-2714-5_37.

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Katayama, Katsuki, Masafumi Yano, and Tsuyoshi Horiguchi. "Synchronous Phenomena for Two-Layered Neural Network with Chaotic Neurons." In Neural Information Processing. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-30499-9_3.

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Ritz, Raphael, Wulfram Gerstner, and J. Leo van Hemmen. "Associative Binding and Segregation in a Network of Spiking Neurons." In Models of Neural Networks. Springer New York, 1994. http://dx.doi.org/10.1007/978-1-4612-4320-5_5.

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Adachi, Masaharu. "Surrogating Neurons in an Associative Chaotic Neural Network." In Advances in Neural Networks – ISNN 2004. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-28647-9_37.

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Romero, G., P. A. Castillo, J. J. Merelo, and A. Prieto. "Using SOM for Neural Network Visualization." In Connectionist Models of Neurons, Learning Processes, and Artificial Intelligence. Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-45720-8_75.

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Payne, Jeremy R., Zhian Xu, and Mark E. Nelson. "A Network Model of Automatic Gain Control in the Electrosensory System." In Computation in Neurons and Neural Systems. Springer US, 1994. http://dx.doi.org/10.1007/978-1-4615-2714-5_33.

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Streszczenia konferencji na temat "NEURONS NEURAL NETWORK"

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Bian, Shaoping, Kebin Xu, and Jing Hong. "Near neighbor neurons interconnected neural network." In OSA Annual Meeting. Optica Publishing Group, 1989. http://dx.doi.org/10.1364/oam.1989.tht27.

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When the Hopfield neural network is extended to deal with a 2-D image composed of N×N pixels, the weight interconnection is a fourth-rank tensor with N4 elements. Each neuron is interconnected with all other neurons of the network. For an image, N will be large. So N4, the number of elements of the interconnection tensor, will be so large as to make the neural network's learning time (which corresponds to the precalculation of the interconnection tensor elements) too long. It is also difficult to implement the 2-D Hopfield neural network optically.
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FILO, G. "Analysis of Neural Network Structure for Implementation of the Prescriptive Maintenance Strategy." In Terotechnology XII. Materials Research Forum LLC, 2022. http://dx.doi.org/10.21741/9781644902059-40.

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Abstract. This paper provides an initial analysis of neural network implementation possibilities in practical implementations of the prescriptive maintenance strategy. The main issues covered are the preparation and processing of input data, the choice of artificial neural network architecture and the models of neurons used in each layer. The methods of categorisation and normalisation within each distinguished category were proposed in input data. Based on the normalisation results, it was suggested to use specific neuron activation functions. As part of the network structure, the applied sol
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Zheng, Shengjie, Lang Qian, Pingsheng Li, Chenggang He, Xiaoqi Qin, and Xiaojian Li. "An Introductory Review of Spiking Neural Network and Artificial Neural Network: From Biological Intelligence to Artificial Intelligence." In 8th International Conference on Artificial Intelligence (ARIN 2022). Academy and Industry Research Collaboration Center (AIRCC), 2022. http://dx.doi.org/10.5121/csit.2022.121010.

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Stemming from the rapid development of artificial intelligence, which has gained expansive success in pattern recognition, robotics, and bioinformatics, neuroscience is also gaining tremendous progress. A kind of spiking neural network with biological interpretability is gradually receiving wide attention, and this kind of neural network is also regarded as one of the directions toward general artificial intelligence. This review summarizes the basic properties of artificial neural networks as well as spiking neural networks. Our focus is on the biological background and theoretical basis of s
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Sharpe, J. P., K. M. Johnson, and M. G. Robinson. "Large scale simulations of an optoelectronic neural network." In OSA Annual Meeting. Optica Publishing Group, 1992. http://dx.doi.org/10.1364/oam.1992.mbb4.

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With improving optical device technology it is now possible to consider constructing very large optoelectronic neural networks containing of the order of 1000 fully interconnected neurons. Whether such systems will work, though, depends on the quality of the optical devices and the network architecture. A recent study has indicated that a 200 neuron single layer polarization logic network can operate under the constraints of presently available spatial light modulators.1 In this paper we will extend this study to examine the effect of device limitations on the performance of single- and multil
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Huynh, Alex V., John F. Walkup, and Thomas F. Krile. "Optical perceptron-based quadratic neural network." In OSA Annual Meeting. Optica Publishing Group, 1991. http://dx.doi.org/10.1364/oam.1991.mii8.

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Optical quadratic neural networks are currently being investigated because of their advantages over linear neural networks.1 Based on a quadratic neuron already constructed,2 an optical quadratic neural network utilizing four-wave mixing in photorefractive barium titanate (BaTiO3) has been developed. This network implements a feedback loop using a charge-coupled device camera, two monochrome liquid crystal televisions, a computer, and various optical elements. For training, the network employs the supervised quadratic Perceptron algorithm to associate binary-valued input vectors with specified
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Farhat, Nabil H., and Mostafa Eldefrawy. "The bifurcating neuron." In OSA Annual Meeting. Optica Publishing Group, 1991. http://dx.doi.org/10.1364/oam.1991.mk3.

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Present neural network models ignore temporal considerations, and hence synchronicity in neural networks, by representing neuron response with a transfer function relating frequency of action potentials (firing frequency) to activation potential. Models of living neuron based on the Hudgkin-Huxley model of the excitable membrane of the squid’s axon and its Fitzhugh-Nagumo approximation, exhibit much more complex and rich behavior than that described by firing frequency-activation potential models. We describe the theory, operation, and properties of an integrate-and-fire neuron which we call t
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Zhan, Tiffany. "Hyper-Parameter Tuning in Deep Neural Network Learning." In 8th International Conference on Artificial Intelligence and Applications (AI 2022). Academy and Industry Research Collaboration Center (AIRCC), 2022. http://dx.doi.org/10.5121/csit.2022.121809.

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Deep learning has been increasingly used in various applications such as image and video recognition, recommender systems, image classification, image segmentation, medical image analysis, natural language processing, brain–computer interfaces, and financial time series. In deep learning, a convolutional neural network (CNN) is regularized versions of multilayer perceptrons. Multilayer perceptrons usually mean fully connected networks, that is, each neuron in one layer is connected to all neurons in the next layer. The full connectivity of these networks makes them prone to overfitting data. T
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Lin, Steven, Demetri Psaltis, and Jae Kim. "High-gain GaAs optoelectronic thresholding devices for neural network implementation." In Integrated Photonics Research. Optica Publishing Group, 1991. http://dx.doi.org/10.1364/ipr.1991.tuc1.

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The optical implementation of a neural network consists of two basic components: a 2-D array of neurons and interconnections. Each neuron is a nonlinear processing element that, in its simplest form, produces an output which is the thresholded version of the input. Monolithic optoelectronic integrated devices are candidates for these neurons. However, in order for these devices to be used as neurons in a practical experiment, they must be large in number (104/cm2 - 106/cm2) and exhibit high gain. This puts a stringent requirement on the electrical power dissipation. Thus, these devices have to
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Wright, John A., Svetlana Tatic-Lucic, Yu-Chong Tai, Michael P. Maher, Hannah Dvorak, and Jerome Pine. "Towards a Functional MEMS Neurowell by Physiological Experimentation." In ASME 1996 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 1996. http://dx.doi.org/10.1115/imece1996-1370.

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Abstract Specificity in neuron targeting during stimulation and recording is essential in executing complex neuronal network studies. Physiological experiments have shown that young (less than one week old) cultured neurons can escape through a 0.5 μm-square hole. Through iterative physiological experimentation, a functional MEMS structure we call a canopy neurowell has been developed. Essentially a micromechanical cage, the structure consists of a well, an electrode bottom, and a nitride canopy cover with integrated micro-tunnels. It is able to physically confine, and make electrical contact
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Petrisor, G. C., B. K. Jenkins, H. Chin, and A. R. Tanguay. "Dual-function adaptive neural networks for photonic implementation." In OSA Annual Meeting. Optica Publishing Group, 1990. http://dx.doi.org/10.1364/oam.1990.mvv1.

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We present an adaptive neural-network model in which each neuron unit has two inputs, a single potential, and two outputs. This network model can exhibit general functionality, although it uses only nonnegative values for all signals and weights in the interconnection. In this dual-function network (DFN), each output is a distinct nonlinear function of the potential of the neuron. Each neuron-unit input receives a linear combination of both outputs from any set of neurons in the preceding layer. A learning algorithm for multilayer feedforward DFN's based on gradient descent of an error functio
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Raporty organizacyjne na temat "NEURONS NEURAL NETWORK"

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Markova, Oksana, Serhiy Semerikov та Maiia Popel. СoCalc as a Learning Tool for Neural Network Simulation in the Special Course “Foundations of Mathematic Informatics”. Sun SITE Central Europe, 2018. http://dx.doi.org/10.31812/0564/2250.

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The role of neural network modeling in the learning сontent of special course “Foundations of Mathematic Informatics” was discussed. The course was developed for the students of technical universities – future IT-specialists and directed to breaking the gap between theoretic computer science and it’s applied applications: software, system and computing engineering. CoCalc was justified as a learning tool of mathematical informatics in general and neural network modeling in particular. The elements of technique of using CoCalc at studying topic “Neural network and pattern recognition” of the sp
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Tarasenko, Andrii O., Yuriy V. Yakimov, and Vladimir N. Soloviev. Convolutional neural networks for image classification. [б. в.], 2020. http://dx.doi.org/10.31812/123456789/3682.

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This paper shows the theoretical basis for the creation of convolutional neural networks for image classification and their application in practice. To achieve the goal, the main types of neural networks were considered, starting from the structure of a simple neuron to the convolutional multilayer network necessary for the solution of this problem. It shows the stages of the structure of training data, the training cycle of the network, as well as calculations of errors in recognition at the stage of training and verification. At the end of the work the results of network training, calculatio
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Cárdenas-Cárdenas, Julián Alonso, Deicy J. Cristiano-Botia, and Nicolás Martínez-Cortés. Colombian inflation forecast using Long Short-Term Memory approach. Banco de la República, 2023. http://dx.doi.org/10.32468/be.1241.

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We use Long Short Term Memory (LSTM) neural networks, a deep learning technique, to forecast Colombian headline inflation one year ahead through two approaches. The first one uses only information from the target variable, while the second one incorporates additional information from some relevant variables. We employ sample rolling to the traditional neuronal network construction process, selecting the hyperparameters with criteria for minimizing the forecast error. Our results show a better forecasting capacity of the network with information from additional variables, surpassing both the ot
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Williams, J. G., and W. C. Jouse. Drive reinforcement neurals networks for reactor control. Final report. Office of Scientific and Technical Information (OSTI), 1995. http://dx.doi.org/10.2172/114638.

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Tam, David C. A Study of Neuronal Properties, Synaptic Plasticity and Network Interactions Using a Computer Reconstituted Neuronal Network Derived from Fundamental Biophysical Principles. Defense Technical Information Center, 1992. http://dx.doi.org/10.21236/ada257221.

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Tam, David C. A Study of Neuronal Properties, Synaptic Plasticity and Network Interactions Using a Computer Reconstituted Neuronal Network Derived from Fundamental Biophysical Principles. Defense Technical Information Center, 1990. http://dx.doi.org/10.21236/ada230477.

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Kater, S. B., and Barbara C. Hayes. Circuit Behavior in the Development of Neuronal Networks. Defense Technical Information Center, 1988. http://dx.doi.org/10.21236/ada198040.

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Brown, Thomas H. Long-Term Synaptic Plasticity and Learning in Neuronal Networks. Defense Technical Information Center, 1986. http://dx.doi.org/10.21236/ada173170.

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Berger, Theodore W. A Systems Theoretic Investigation of Neuronal Network Properties of the Hippocampal Formation. Defense Technical Information Center, 1991. http://dx.doi.org/10.21236/ada250246.

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Rulkov, Nikolai. Nonlinear Maps for Design of Discrete Time Models of Neuronal Network Dynamics. Defense Technical Information Center, 2016. http://dx.doi.org/10.21236/ad1004577.

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