M. Balsi, I. Ciancaglioni, V. Cimagalli, F. Galluzzi
{"title":"Optoelectronic cellular neural networks based on amorphous silicon thin film technology","authors":"M. Balsi, I. Ciancaglioni, V. Cimagalli, F. Galluzzi","doi":"10.1109/CNNA.1994.381642","DOIUrl":"https://doi.org/10.1109/CNNA.1994.381642","url":null,"abstract":"In this paper, hardware realization of cellular neural networks in amorphous silicon thin film technology is proposed. In this way, it is possible to realize inexpensive large-scale, easily programmable circuits, with integrated light-sensing and light-emitting devices.<<ETX>>","PeriodicalId":248898,"journal":{"name":"Proceedings of the Third IEEE International Workshop on Cellular Neural Networks and their Applications (CNNA-94)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1994-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114473751","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
P. L. Venetianer, P. Szolgay, K. R. Crounse, T. Roska, L. Chua
{"title":"Analog combinatorics and cellular automata-key algorithms and layout design","authors":"P. L. Venetianer, P. Szolgay, K. R. Crounse, T. Roska, L. Chua","doi":"10.1109/CNNA.1994.381671","DOIUrl":"https://doi.org/10.1109/CNNA.1994.381671","url":null,"abstract":"This paper demonstrates how certain logic and combinatorial tasks can be solved using CNNs. The most important application generalizes a shortest path algorithm to design the layout of printed circuit boards. Besides, it is shown how cellular automata can be simulated on CNN, and tasks, such as sorting, parity analysis, histogram calculation of black-and-white images, and computing minimum Hamming distance are also solved.<<ETX>>","PeriodicalId":248898,"journal":{"name":"Proceedings of the Third IEEE International Workshop on Cellular Neural Networks and their Applications (CNNA-94)","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1994-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130263358","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Digitally controllable weights in current mode cellular neural networks","authors":"A. Paasio, A. Dawidziuk, K. Halonen, V. Porra","doi":"10.1109/CNNA.1994.381704","DOIUrl":"https://doi.org/10.1109/CNNA.1994.381704","url":null,"abstract":"Current mode CNN with adjustable weights is discussed. Two main possible solutions are considered: continuous and discrete control. The solutions are compared on very general level and the discrete control is taken into detailed investigation. A test chip has been designed. Simulation and measurement results are reported.<<ETX>>","PeriodicalId":248898,"journal":{"name":"Proceedings of the Third IEEE International Workshop on Cellular Neural Networks and their Applications (CNNA-94)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1994-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130574639","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Synchronization phenomena in 2D chaotic CNN","authors":"S. Jankowski, A. Londei, C. Mazur, A. Lozowski","doi":"10.1109/CNNA.1994.381656","DOIUrl":"https://doi.org/10.1109/CNNA.1994.381656","url":null,"abstract":"Complex pattern formation in two-dimensional cellular network of chaotic oscillators is presented in the paper. The patterns are related to unstable periodic orbits of the network chaotic dynamics and may be formed in the synchronization process obtained by means of chaos suppression. This effect can be considered as transition from turbulent phase to partially synchronized phase in the network.<<ETX>>","PeriodicalId":248898,"journal":{"name":"Proceedings of the Third IEEE International Workshop on Cellular Neural Networks and their Applications (CNNA-94)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1994-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130399726","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Statistical design using variable parameter variances and application to cellular neural networks","authors":"I. Fajfar, F. Bratkovic","doi":"10.1109/CNNA.1994.381693","DOIUrl":"https://doi.org/10.1109/CNNA.1994.381693","url":null,"abstract":"Many cellular neural network design methods result in a set of linear inequalities, which they attempt to solve by various methods. In the paper we first point out the importance of the problem for the CNN design, and then expand the statistical design method proposed by R.K. Brayton, G.D. Hachtel, and S.W. Director (1978), applying it to cellular neural networks. Instead of original assumption of constant variances of the statistical parameter distributions, we take variances to be linearly dependent on parameter nominal values, which leads us to construct an iterative process with very fast convergence. A design example of winner-take-all cellular neural network is given, showing that with our improvement one can reliably implement the network of up to 50 cells as opposed to 10 cell CNN obtained by the original method.<<ETX>>","PeriodicalId":248898,"journal":{"name":"Proceedings of the Third IEEE International Workshop on Cellular Neural Networks and their Applications (CNNA-94)","volume":"54 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1994-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130707410","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Cooperative and competitive cellular neural networks","authors":"M. Tanaka, T. Watanabe","doi":"10.1109/CNNA.1994.381696","DOIUrl":"https://doi.org/10.1109/CNNA.1994.381696","url":null,"abstract":"The association and recognition in a brain are done in parallel by global processing after distributed local processing. Therefore, it is very interesting to design a cellular neural network (CNN) which can unify globally local information. This paper describes a generalized cooperative and competitive cellular neural network and its applications to associative memory and pattern recognition.<<ETX>>","PeriodicalId":248898,"journal":{"name":"Proceedings of the Third IEEE International Workshop on Cellular Neural Networks and their Applications (CNNA-94)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1994-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132485398","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Hubert Harrer, P. Venetianer, J. Nossek, T. Roska, L. O. Chua
{"title":"Some examples of preprocessing analog images with discrete-time cellular neural networks","authors":"Hubert Harrer, P. Venetianer, J. Nossek, T. Roska, L. O. Chua","doi":"10.1109/CNNA.1994.381679","DOIUrl":"https://doi.org/10.1109/CNNA.1994.381679","url":null,"abstract":"The paper gives two examples, where an analog input image is preprocessed by a sequence of templates, i.e. by analogic CNN algorithms running on the CNN Universal Machine. The examples are: the extraction of horizontal screws with arbitrary length and the classification of screws according to their size.<<ETX>>","PeriodicalId":248898,"journal":{"name":"Proceedings of the Third IEEE International Workshop on Cellular Neural Networks and their Applications (CNNA-94)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1994-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133247891","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
K. Lotz, Zoltán Vidnyánszky, T. Roskar, Joos P. Vandewalle, J. Hámori, A. Jacobs, F. Werblin
{"title":"Some cortical spiking neuron models using CNN","authors":"K. Lotz, Zoltán Vidnyánszky, T. Roskar, Joos P. Vandewalle, J. Hámori, A. Jacobs, F. Werblin","doi":"10.1109/CNNA.1994.381709","DOIUrl":"https://doi.org/10.1109/CNNA.1994.381709","url":null,"abstract":"In this paper we show cellular neural network (CNN) models of some basic types of cells characterised by diverse spiking patterns. After showing some preliminary models (ion channels, neurons), CNN models of the action potential generation are given followed by an analysis of the rate coding capabilities of the models. Furthermore, CNN models of neurons with diverse intrinsic firing patterns are presented followed by some conclusions.<<ETX>>","PeriodicalId":248898,"journal":{"name":"Proceedings of the Third IEEE International Workshop on Cellular Neural Networks and their Applications (CNNA-94)","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1994-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133451301","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"A double time-scale CNN for solving 2-D Navier-Stokes equations","authors":"T. Kozek, T. Roska","doi":"10.1109/CNNA.1994.381668","DOIUrl":"https://doi.org/10.1109/CNNA.1994.381668","url":null,"abstract":"A practical cellular neural network (CNN) approximation to the Navier Stokes equation describing viscous flow of incompressible fluids is presented. The implementation of the CNN templates based on a finite difference discretization scheme, including the double time-scale CNN dynamics and the treatment of various types of boundary conditions are explained. The operation of the continuous time model is demonstrated through several examples.<<ETX>>","PeriodicalId":248898,"journal":{"name":"Proceedings of the Third IEEE International Workshop on Cellular Neural Networks and their Applications (CNNA-94)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1994-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132138470","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
R. Domínguez-Castro, S. Espejo, Á. Rodríguez-Vázquez, I. Garcia-Vargas, J. Ramos, R. Carmona
{"title":"SIRENA: a simulation environment for CNNs","authors":"R. Domínguez-Castro, S. Espejo, Á. Rodríguez-Vázquez, I. Garcia-Vargas, J. Ramos, R. Carmona","doi":"10.1109/CNNA.1994.381639","DOIUrl":"https://doi.org/10.1109/CNNA.1994.381639","url":null,"abstract":"SIRENA is a general simulation environment for artificial neural networks, with emphasis towards CNNs. A special interest has been placed in allowing the simulation and modelling of the non-ideal effects expected from VLSI implementations. SIRENA allows the simulation of CNNs in greater detail than conventional CNN simulators, and much more efficiently than SPICE-type electrical simulators.<<ETX>>","PeriodicalId":248898,"journal":{"name":"Proceedings of the Third IEEE International Workshop on Cellular Neural Networks and their Applications (CNNA-94)","volume":"219 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1994-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122848494","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}