# Delta Learning Rule for the Active Sites Model

- Published: 02 Jul 2010

- 1
- 2

[1] S. Kak, Feedback neural networks: new characteristics and a generalization. Circuits, Systems, and Signal Processing, vol. 12, pp. 263-278, 1993.

[2] K.C. Lingashetty, Active sites model for the B-matrix approach. 2010. arXiv:1006.4754 [3] S. Kak, Single neuron memories and the network‟s proximity matrix. 2009. arXiv:0906.0798 [4] J.J. Hopfield, Neural networks and physical systems with emergent collective computational properties. Proc. Nat. Acad. Sci. (USA), vol. 79, pp. 2554-2558, 1982. [OpenAIRE]

[5] D.L. Prados and S. Kak, Neural network capacity using the delta rule. Electronics Letters, vol. 25, pp. 197-199, 1989. [OpenAIRE]

[6] S. Kak, The three languages of the brain: quantum, reorganizational, and associative. In: K. Pribram, J. King (Eds.), Learning as Self- Organization, Lawrence Erlbaum, London, 1996, pp. 185-219.

[7] R. J. McEliece, E. C. Posner, E. R. Rodemich, and S. S. Venkatesh, The capacity of the Hopfield associative memory, IEEE Trans. Inform.Theory, vol. IT-33, pp. 461-482, 1987. [OpenAIRE]

[8] K.H. Pribram and J. L. King (eds.), Learning as Self-Organization. Mahwah, N. J.: L. Erlbaum Associates, 1996.

[9] D. Prados and S. Kak, Non-binary neural networks. Lecture Notes in Computing and Control, vol. 130, pp. 97-104, 1989.

[10] M. Schuster and K. K. Paliwal. Bidirectional recurrent neural networks. IEEE Transactions on Signal Processing, vol. 45, pp. 2673-2681, 1997.

[11] C. Ji and D. Psaltis, Capacity of two-layer feedforward neural networks with binary weights. IEEE Trans. Inform. Theory, vol. 44, pp. 256-268, 1998.

[12] S. Kak, Can we define levels of artificial intelligence? Journal of Intelligent Systems, vol. 6, pp.133-144, 1996.

[13] S. Kak, Artificial and biological intelligence. ACM Ubiquity, vol. 6, number 42, pp. 1-20, 2005. [OpenAIRE]

[14] D.L. Schacter, Searching for Memory: The Brain, the Mind, and the Past. Basic Books, New York, 1997. [OpenAIRE]

[15] G.D.A. Brown, I. Neath, N. Chater, A ratio model of scale-invariant memory and identification. Psychological Review, vol. 114, pp. 539-576, 2007. [OpenAIRE]

[16] S. Kak, New training algorithm in feedforward neural networks, First International Conference on Fuzzy Theory and Technology, Durham, N. C., October 1992. Also in Wang, P.P. (Editor), Advance in fuzzy theory and technologies, Durham, N. C. Bookwright Press, 1993.

[17] S. Kak, A class of instantaneously trained neural networks, Information Sciences, 148, 97- 102, 2002. [OpenAIRE]

- 1
- 2