Review of stability properties of neural plasticity rules for implementation on memristive neuromorphic hardware

Proceedings of the International Joint Conference on Neural Networks

Volumn , Issue , 2011, Pages 2563-2569

  Vasilkoski, Zlatko ^b   Ames, Heather ^a   Chandler, Ben ^a   Gorchetchnikov, Anatoli ^a   Léveillé, Jasmin ^a   Livitz, Gennady ^a   Mingolla, Ennio ^a   Versace, Massimiliano ^a  

^a BOSTON UNIVERSITY   (United States)

^b HARVARD MEDICAL SCHOOL   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ADAPTIVE BEHAVIOR; COMPLEX BEHAVIOR; HIGH-DENSITY; LEARNING RULES; NEURAL ACTIVITY; NEURAL PLASTICITY; NEURAL SYSTEMS; NEUROMORPHIC; PARALLEL HARDWARE; ROBOTIC AGENTS; STABILITY PROPERTIES; SYNAPTIC CONNECTIONS;

MATHEMATICAL MODELS; STABILITY;

NEURAL NETWORKS;

EID: 80054733639     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/IJCNN.2011.6033553     Document Type: Conference Paper

References (18)

1. A. Gorchetchnikov, M. Versace, H. M. Ames, B. Chandler, J. Léveillé, G. Livitz, E. Mingolla, G. Snider, R. Amerson, D. Carter, H. Abdalla, and M. S. Qureshi, "Review and unification of learning framework in cog ex machina platform for memristive neuromorphic hardware," in Proceedings of the International Joint Conference on Neural Networks, 2011, this volume.
2. L. Chua and S. M. Kang, "Memristive devices and systems," Proceedings of IEEE, vol. 64, no. 2, pp. 388-398, 1976.
3. D. B. Strukov, G. S. Snider, D. R. Stewart, and R. S. Williams, "The missing memristor found," Nature, vol. 453, pp. 80-83, 2008.
4. 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.
5. M. Versace and B. Chandler, "MoNETA: A mind made from memristors," IEEE Spectrum, vol. 47, no. 12, pp. 30-37, 2010.
6. D. O. Hebb, The Organization of Behavior. New York, NY: Wiley, 1949.
7. 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.
8. E. Oja, "A simplified neuron model as a principal component analyzer," J Math Biol, vol. 15, pp. 267-273, 1982.
9. S. Grossberg, S. Hwang, and E. Mingolla, "Thalamocortical dynamics of the McCollough effect: Boundary-surface alignment through perceptual learning," Vision Research, vol. 42, pp. 1259-1286, 2002.
10. P. Dayan and L. F. Abbott, Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems. Cambridge, MA: MIT Press, 2001.
11. 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.
12. 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.
13. 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.
14. R. Sutton and A. Barto, Reinforcement Learning. Cambridge, MA: MIT Press, 1998.
15. 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.
16. P. Földiák, "Forming sparse representations by local anti-Hebbian learning," Biol Cybern, vol. 64, pp. 165-170, 1990.
17. -, "Learning invariance from transformation sequences," Neural Comput, vol. 3, pp. 194-200, 1991.
18. J. L. Wyatt and I. M. Elfadel, "Time-domain solutions of Ojas equations," Neural Comput, vol. 7, no. 5, pp. 915-922, 1995.