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Vol: 47(61) No: 2 / June 2002 Neural Nets Activity From Neuroinformatics Theory Viewpoint Adriana - Meda Truta Department of Human-science and Communication, National Intelligence Academy, Sos. Odai nr. 20-22, Bucharest, Romania, phone: 04-01-7786134, e-mail: meda_truta@k.ro Mihai Slate Department of Human-science and Communication, National Intelligence Academy, Sos. Odai nr. 20-22, Bucharest, Romania, e-mail: misus2@yahoo.com Keywords: neural networks, negentropy, posentropy. Abstract Neuroinformatics is a multidiscipline that results from synergetic actions of several theories such as achievement, processing, storage, transmission, recovery and diffusion of neural information. From neuroinformatics point of view, the neural complex (natural or artificial neural nets) is considered an automata with self-control, a memory machine and hemostats (hemostats represent the whole internal processes and behavior that have as a main goal the achievement of an equilibrium state in several changes of environment). Neural nets (natural or artificial) are neural complex systems with C3I protocol (commands, communication, control and information). Neural nets consist of cellular units strongly interconnected. Excitatory/inhibitory activities of cellular unit propagate information to the entire system. Parallel information processing in these units leads to network convergence by cost function minimizing. Neural activity is described by the percentage of the excitatory/inhibitory cellular units. The excitatory activity is described as negentropy (the uncertainty parameter) and the inhibitory activity is described as posentropy (the certainty parameter). References [1] D. L. Ruderman, Network 5(4), 517 (1995), R. J. Baddeley and P. J. B. Hancock, Proc. Roy. Soc. B 246, 219 (1991), J. J. Atick, Network 3, 213 (1992). [2] S. Laughlin, Z. Naturforsch. 36c, 910 (1981). [3] Purves, D. Neural activity and the growth of the brain, (Cambridge University Press, NY, 1994); X. Gu and N. C. Spitzer, Nature 375, 784 (1995). [4] G. LeMasson, E. Marder, and L. F. Abbott, Science 259, 1915 (1993). [5] A. J. Bell, Neural Information Processing Systems 4, 59 (1992). [6] D. O. Hebb, The Organization of Behavior (Wiley, New York, 1949). [7] All parameters for the somatic compartment, with the exception of the adaptation conductance, are given by the standard model of J. A. Connor, D. Walter, R. McKown, Biophys. J. 18, 81 (1977). [8] R. B. Stein, Biophys. J. 7, 797 (1967). Equality holds asymptotically, since the distribution of firing rates in response to a constant stimulus x approaches a Gaussian distribution over long times. [9] M. S. Pinsker, Information and information stability of random variables and processes (Holden-Day, San Francisco, 1964); [10] R. B. Avery and D. Johnston, J. Neurosci. 16, 5567 (1996), F. Helmchen, K. Imoto, and B. Sakmann, Biophys. J. 70, 1069 (1996); [11] F. Hofmann, M. Biel, and V. Flockerzi, Ann. Rev. Neurosci. 17, 399 (1994); [12] Y. Z. Tsypkin, Adaptation and Learning in Automatic Systems (Academic Press, NY, 1971)]; [13] R. Linsker, Neural Comp. 4(5), 691 (1992), and A. J. Bell and T. J. Sejnowski, Neural Comp. 7(6), 1129 (1995). |