Review and unification of learning framework in Cog Ex Machina platform for memristive neuromorphic hardware

Proceedings of the International Joint Conference on Neural Networks

Volumn , Issue , 2011, Pages 2601-2608

  Gorchetchnikov, Anatoli ^a   Versace, Massimiliano ^a   Ames, Heather ^a   Chandler, Ben ^a   Léveillé, Jasmin ^a   Livitz, Gennady ^a   Mingolla, Ennio ^a   Snider, Greg ^b   Amerson, Rick ^b   Carter, Dick ^b   Abdalla, Hisham ^b   Qureshi, Muhammad Shakeel ^b  

^a BOSTON UNIVERSITY   (United States)

^b HEWLETT PACKARD LABORATORIES   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ADAPTIVE BEHAVIOR; BRAIN FUNCTIONS; HEBB RULES; LARGE ARRAYS; LEARNING FRAMEWORKS; LEARNING RULES; LOCAL LEARNING; LOW POWER; MOTOR BEHAVIOR; NEURAL ACTIVITY; NEURAL MODELS; NEUROMORPHIC; PARAMETERIZATIONS; PARAMETERIZED; RESISTIVE STATE; SPATIAL SCALE; SYNAPTIC CONNECTIONS; THRESHOLD RULE;

HARDWARE; NEURAL NETWORKS;

COMPUTER HARDWARE;

EID: 80054743547     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/IJCNN.2011.6033558     Document Type: Conference Paper

References (23)

1. G. Snider, "Instar and outstar learning with memristive nanodevices," Nanotechnology, vol. 22, no. 1, p. 015201, 2011.
2. G. Snider, R. Amerson, D. Carter, H. Abdalla, M. S. Qureshi, J. Léveillé, M. Versace, H. M. Ames, S. Patrick, B. Chandler, A. Gorchetchnikov, and E. Mingolla, "From synapses to circuitry: Using memristive memory to explore the electronic brain," IEEE Computer, vol. 11, pp. 37-44, 2011.
3. M. Versace and B. Chandler, "MoNETA: A mind made from memristors," IEEE Spectrum, vol. 47, no. 12, pp. 30-37, 2010.
4. D. O. Hebb, The Organization of Behavior. New York, NY: Wiley, 1949.
5. S. Grossberg, "Adaptive pattern classification and universal recoding i: Parallel development and coding of neural feature detectors," Biol Cybern, vol. 23, pp. 121-134, 1976.
6. E. Oja, "A simplified neuron model as a principal component analyzer," J Math Biol, vol. 15, pp. 267-273, 1982.
7. E. L. Bienenstock, L. Cooper, and P. Munro, "Theory for the development of neuron selectivity: Orientation specificity and binocular interaction in visual cortex," J Neurosci, vol. 2, no. 1, pp. 31-48, 1982.
8. N. Intrator and L. N. Cooper, "Objective function formulation of the BCM theory of visual cortical plasticity: Statistical connections, stability conditions," Neural Netw, vol. 5, pp. 3-17, 1992.
9. C. Law and L. Cooper, "Formation of receptive fields according to the BCM theory in realistic visual environments," in Proc Natl Acad Sci USA, vol. 91, 1994, pp. 7797-7801.
10. P. Dayan and L. F. Abbott, Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems. Cambridge, MA: MIT Press, 2001.
11. A. E. Bryson and Y. C. Ho, Applied Optimal Control: Optimization, Estimation, and Control. New York: Blaisdell Publishing Company, 1969.
12. G. F. Harpur and R. W. Prager, "Development of low entropy coding in a recurrent network," Computation in Neural Systems, vol. 7, no. 2, pp. 277-284, 1996.
13. G. E. Hinton, "Training products of experts by minimizing contrastive divergence," Neural Comput, vol. 14, no. 8, pp. 1771-1800, 2002.
14. D. Rumelhart, G. Hinton, and R. Williams, "Learning internal representations by error propagation," in Parallel Distributed Processing: Explorations in the Microstructure of Cognition, D. Rumelhart, J. Mc-Clelland, and P. D. P. Research Group, Eds. Cambridge, MA: MIT Press/Bratford Books, 1986, vol. 1, pp. 194-281.
15. P. Földiák, "Forming sparse representations by local anti-Hebbian learning," Biol Cybern, vol. 64, pp. 165-170, 1990.
16. -, "Learning invariance from transformation sequences," Neural Comput, vol. 3, pp. 194-200, 1991.
17. R. A. Rescorla and A. R. Wagner, "A theory of pavlovian conditioning: Variations in the effectiveness of reinforcement and nonreinforcement," in Classical Conditioning II, A. H. Black and W. F. Prokasy, Eds. New York, NY: Appleton-Century-Crofts, 1972, pp. 64-99.
18. R. Sutton and A. Barto, Reinforcement Learning. Cambridge, MA: MIT Press, 1998.
19. W. B. Levy and O. Steward, "Temporal contiguity requirements for long-term associative potentiation/depression in the hippocampus," Neuroscience, vol. 8, no. 4, pp. 791-797, 1983.
20. A. Gorchetchnikov, M. Versace, and M. E. Hasselmo, "Spatially and temporally local spike-timing-dependent plasticity rule," in Proceedings of the International Joint Conference on Neural Networks, Montreal, Canada, 2005, july 31 - August 4, 2005.
21. J. L. Elman, "Finding structure in time," Cogn Sci, vol. 14, pp. 179-211, 1990.
22. M. I. Jordan, "Attractor dynamics and parallelism in a connectionist sequential machine," in Proceedings of the Eighth Annual Conference of the Cognitive Science Society, 1986, pp. 531-586.
23. M. Tsukada and Y. Yamazaki, "Functional differences between the spatio-temporal learning rule (STLR) and Hebb type (HEBB) in single pyramidal cells in the hippocampal CA1 area," in Neural Information Processing, ser. Lecture Notes in Computer Science, I. King, J. Wang, L.-W. Chan, and D. Wang, Eds. Springer Berlin / Heidelberg, 2006, vol. 4232, pp. 72-81.