Learning in lattice neural networks that employ dendritic computing

Studies in Computational Intelligence

Volumn 67, Issue , 2007, Pages 25-44

  Ritter, Gerhard X ^a   Urcid, Gonzalo ^b  

^a University of Florida   (United States)

^b INSTITUTO NACIONAL DE ASTROFÍSICA   (Mexico)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 34347406505     PISSN: 1860949X     EISSN: None     Source Type: Book Series    
DOI: 10.1007/978-3-540-72687-6_2     Document Type: Article

References (24)

1. Eccles JC (1977) The Understanding of the Brain, McGraw-Hill, New York, NY
2. Kaburlasos VG (2003) Improved fuzzy lattice neurocomputing for semantic neural computing. In: Proc Inter Joint Conf on Neural Networks pp 1850-1855
3. Keller JM, Hunt DJ (1985) Incorporating fuzzy membership functions into the perceptron algorithm. IEEE Trans Pattern Analysis and Machine Intelligence 7:693-699
4. Koch C, Segev I (1989) Methods in Neuronal Modeling: From Synapses to Networks. MIT Press, Cambridge, MA
5. Marks RJ, Oh S, Arabshahi P et al (1992) Steepest descent adaption of min-max fuzzy if-then rules. In: Proc Inter Joint Conf on Neural Networks 3:471-477
6. Myers DS (2006) The synaptic morphological perceptron. In: Ritter GX, Schmalz MS, Barrera J, Astola JT (eds) Proc SPIE Math of Data/Image Pattern Recognition, Compr and Encryp 6315 pp 63150B:1-11
7. Petridis V, Kaburlasos VG (1998) Fuzzy lattice neural networks: A hybrid model for learning. IEEE Trans on Neural Networks 9:877-890
8. Rall W, Segev I (1987) Functional possibilities for synapses on dendrites and dendritic spines. In: Edelman GM, Gall EE, Cowan WM (eds) Synaptic Function. Wiley, New York, NY
9. Ritter GX, Sussner P (1997) Associative memories based on lattice algebra. In: Proc IEEE Inter Conf on Sys, Man & Cyber pp 3570-3575
10. Ritter GX, Sussner P, Diaz de Leon JL (1998) Morphological associative memories. IEEE Trans on Neural Networks 9:281-293
11. Ritter GX, Diaz de Leon JL, Sussner P (1999) Morphological bidirectional associative memories. Neural Networks 12:851-867
12. Ritter GX, Urcid G, Iancu L (2003) Reconstruction of noisy patterns using morphological associative memories. J Math Im & Vision 19:95-111
13. Ritter GX, Urcid G (2003) Lattice algebra approach to single neuron computation. IEEE Trans on Neural Networks 14(2):282-295
14. Ritter GX, Iancu L, Urcid G (2003) Morphological perceptrons with dendritic structure. In: Proc FUZZ-IEEE pp 1296-1301
15. Ritter GX, Iancu L (2003) Single layer feedforward neural network based on lattice algebra. In: Proc IJCNN pp 2887-2892
16. Segev I (1998) Dendritic processing. In: ArbibM(ed) Handbook of Brain Theory and Neural Networks. MIT Press, Cambridge, MA
17. Sejnowski TJ, Qian N (1992) Synaptic integration by electro-diffusion in dendritic spines. In: McKenna T, Davis J, Zornetzer SF (eds) Single Neuron Computation. Academic Press, New York, NY
18. Urcid G (2005) Transformations of neural inputs in lattice dendrite computation. In: Proc SPIE Math Methods in Pattern and Image Analysis 5916:201-212
19. Simpson PK (1992) Fuzzy min-max neural networks - part 1: classification. IEEE Trans on Neural Networks 3: 776-786
20. Simpson PK (1993) Fuzzy min-max neural networks - part 2: Clustering. IEEE Trans on Fuzzy Systems 1:32-45
21. Wasnikar VA, Kulkarni AD (2000) Data mining with radial basis functions. In: Dagli CH et al (eds) Intelligent Engineering Systems Through Artificial Neural Networks. ASME Press, New York, NY
22. Won Y, Gader PD, Coffield P (1997) Morphological shared-weight neural networks with applications to automatic target recognition. IEEE Trans on Neural Networks 8:1195-1203
23. Zhang XH, Wong F, Wang PZ (1993) Decision-support neural networks and its application in financial forecast. Bull of Studies and Exchanges on Fuzziness Applications 54:40-49
24. Zhang XH, Hang C, Tan S, Wang PZ (1996) The min-max function differentiation and training of fuzzy neural networks. IEEE Trans on Neural Networks 7:1139-1150