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[1]   Anderson, J.R. (1976). Language, Memory, and Thought. Hillsdale, New Jersey: Lawrence Erlbaum Associates, Publishers.

[2]   Baddeley, A.D. (1982). Your memory. A user's guide. Macmillan Publishing Co., Inc.

[3]   Burns, B.D., (1958). The Mamalian Cerebral Cortex. Arnold.

[4]   Chomsky, N. (1956). Three models for the description of language. I.R.E. Transactions on Information Theory. JT-2, 113-124.

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[6]   Eliashberg, V. (1967). On a class of learning machines. Proceedings of the Conference on Automation in the Pulp & Paper industry, April 1967, Leningrad USSR. Proc of VNIIB, #54, 350-398. (in Russian)

[7]   Eliashberg, V. (1979). The concept of E-machine and the problem of context-dependent behavior. Txu 40-320, US Copyright Office. Download .pdf file.

[8]   Eliashberg, V. (1981). The concept of E-machine: On brain hardware and the algorithms of thinking. Proceedings of the Third Annual Meeting of the Cognitive Science Society, 289-291. Download .pdf file.

[9]   Eliashberg, V. (1982). The states of residual excitation in neural networks as the mechanism of short-term memory and mental set. Abstract. Neuroscience, Supplement to volume 7, p. S63.

[10]   Eliashberg, V. (1988a). Neuron layer with reciprocal inhibition as a mechanism of random choice. Proceedings of the IEEE ICNN 88.

[11]   Eliashberg, V. (1988b). The E-machines: Associative neural networks as nonclassical symbolic processors. Abstract, The First Annual Meeting of INNS.

[12]   Eliashberg, V. (1989). Context-sensitive associative memory: "Residual excitation" in neural networks as the mechanism of STM and mental set. Proceedings of IJCNN-89, June 18-22, 1989, Washington, D.C. vol. I, 67-75. Download .pdf file.

[13]   Eliashberg, V. (1990a). Universal learning neurocomputers. Proceeding of the Fourth Annual parallel processing symposium. California state university, Fullerton. April 4-6, 1990., 181-191. Download .pdf file.

[14]   Eliashberg, V. (1990b). Molecular dynamics of short-term memory. Mathematical and Computer modeling in Science and Technology. vol. 14, 295-299. Download .pdf file.

[15]   Eliashberg, V. (1993). A relationship between neural networks and programmable logic arrays. 0-7803-0999-5/93, IEEE, 1333-1337.

[16]   Eliashberg, V. (2005). Ensembles of membrane proteins as statistical mixed-signal computers. Submitted to IJCNN-2005. Download .pdf file.

[17]   Eliashberg, V. (2002). What Is Working Memory and Mental Imagery? A Robot that Learns to Perform Mental Computations. Web publication, www.brain0.com, Avel Electronics, Palo Alto, California. Download .html file.

[18]   Gerstner, W., Kistler, W.M., (2002). Mathematical formulations of Hebbian learning. Biological Cybernetics, 87, 404-415.

[19]  Hille, B. (2001). Ion Channels of Excitable Membranes. Sinauer Associates. Sunderland, MA

[20]  Kandel, E.R., and Spencer, W.A. (1968). Cellular Neurophysiological Approaches in the Study of Learning. Physiological Rev. 48, 65-134.

[21]  Kosko, B. (1992). Neural Networks and Fuzzy Systems. A Dynamical Systems Approach to Machine Intelligence. Prentice Hall, Englewood Cliffs, NJ 07632

[22]  Marder, E., Thirumalai, V. (2002). Cellular, synaptic and network effects of neuromodulation. Neural Networks 15, 479-493 .

[23]  Rumelhart, D.E., McClelland, J.L. (Eds.) (1986). Parallel Distributed processing: Explorations in the Microstructure of Cognition. Cambridge, MA: MIT Press. (Vols 1 and 2.)

[24]  Sampolinsky, H. (1988). Statistical mechanics of neural networks. Physics Today, December, pp. 70--80.

[25]   Turing, A.M. (1936). On computable numbers, with an application to the Entscheidungsproblem. Proc. London Math. Society, ser. 2, 42

[26]   Vvedensky, N.E. (1901). Excitation, inhibition, and narcosis. In Complete collection of works. USSR, 1953.

[27]   Zopf, G.W., Jr. (1961). Attitude and Context. In "Principles of Self-organization", Pergamon Press, 325-346.