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Information updated and conveyed by the neural network systems

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inscit2006

The document discusses using neural networks and information theory to model how information is conveyed in the brain. It presents a neural network model that was trained on Walsh patterns as stimuli to test how much information could be encoded and decoded. Computational experiments showed the model was able to code and decode information by implementing an artificial neural network, though more research is still needed to apply this to practical communication systems.

Technologie
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Information updated and conveyed by the neural network systems Algis Garliauskas Institute of Mathematics and Informatics,Laboratory of Neuroinformatics,Akademijos 4, LT-8663, Vilnius, Lithuania
Abstract ,[object Object],[object Object]
Introduction ,[object Object],[object Object]
2.Information retrieval from an associative memory ,[object Object],[object Object]
3. Neural network model and computational experiment 3.1. The Walsh patterns as stimuli. Fig.1. Examples of the Walsh patterns
3.2. Modeling strategy of transmitted information by interconnection network s ,[object Object],[object Object]
3.3. Neural network structure and the learning algorithm ,[object Object],[object Object],[object Object]
3.4. Results of modeling Fig.2. The error changes versus epochs of training: (a) Training and testing errors dependent on epochs, (b) The errors with change of learning parameters  
Fig. 3. The error changes versus epochs of training errors: (1) is the training, (2) is testing, and (3) is the output value errors  
Table.1. The amount of information (bits) in the output of ANN.   Numbeof Number of neurons hidden layers 1 5 10 15 20 1 2.8 2.74 0.12 0.2 0.11 2 3.4 0.23 0.17 0.12 0.15 3 5.1 0.15 0.21 0.17 0.15 4 7.0 0.22 0.13 0.14 0.11 6.1 0.44 0.15 0.16 0.14
4. Conclusions ,[object Object],[object Object]
References Abramson, N. 1963. Information Theory and Coding. MCGraw-Hill, New York. Hamming, R.W. 1990. Coding and Information Theory. Prentice-Hall, Englewood Cliffs, New Jersey. Hartley, R.V. 1928, Transmission of Information. Bell System Tech. J., 7, pp.535-564. Garliauskas, A. 2004. Conceptions and Modeling for Transmitted Information Evaluation by ANN. Informatica, vol.15, 4. Klimashauskas, C., Guiver J.and Pelton G. 1989. Neural Computing, 1. Ricke, F., Warland D. and W. Bialek W. 1993. Coding efficiency anf information rates in sensory neurons. Europhysics Letters, 22}, pp.151-156. Shannon, C.E. 1948. A mathematical theory of communication. Bell System Tech. J., pp.379-423, pp.623-656  

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Information updated and conveyed by the neural network systems

  • 1. Information updated and conveyed by the neural network systems Algis Garliauskas Institute of Mathematics and Informatics,Laboratory of Neuroinformatics,Akademijos 4, LT-8663, Vilnius, Lithuania
  • 2.
  • 3.
  • 4.
  • 5. 3. Neural network model and computational experiment 3.1. The Walsh patterns as stimuli. Fig.1. Examples of the Walsh patterns
  • 6.
  • 7.
  • 8. 3.4. Results of modeling Fig.2. The error changes versus epochs of training: (a) Training and testing errors dependent on epochs, (b) The errors with change of learning parameters  
  • 9. Fig. 3. The error changes versus epochs of training errors: (1) is the training, (2) is testing, and (3) is the output value errors  
  • 10. Table.1. The amount of information (bits) in the output of ANN.   Numbeof Number of neurons hidden layers 1 5 10 15 20 1 2.8 2.74 0.12 0.2 0.11 2 3.4 0.23 0.17 0.12 0.15 3 5.1 0.15 0.21 0.17 0.15 4 7.0 0.22 0.13 0.14 0.11 6.1 0.44 0.15 0.16 0.14
  • 11.
  • 12. References Abramson, N. 1963. Information Theory and Coding. MCGraw-Hill, New York. Hamming, R.W. 1990. Coding and Information Theory. Prentice-Hall, Englewood Cliffs, New Jersey. Hartley, R.V. 1928, Transmission of Information. Bell System Tech. J., 7, pp.535-564. Garliauskas, A. 2004. Conceptions and Modeling for Transmitted Information Evaluation by ANN. Informatica, vol.15, 4. Klimashauskas, C., Guiver J.and Pelton G. 1989. Neural Computing, 1. Ricke, F., Warland D. and W. Bialek W. 1993. Coding efficiency anf information rates in sensory neurons. Europhysics Letters, 22}, pp.151-156. Shannon, C.E. 1948. A mathematical theory of communication. Bell System Tech. J., pp.379-423, pp.623-656