A memory frontier for complex synapses

Advances in Neural Information Processing Systems

Volumn , Issue , 2013, Pages

  Lahiri, Subhaneil ^a   Ganguli, Surya ^a  

^a STANFORD UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

DYNAMICAL SYSTEMS; MATHEMATICAL TECHNIQUES;

FIRST PASSAGE TIME; LEARNING AND MEMORY; MOLECULAR COMPLEXITY; MOLECULAR NETWORKS; MOLECULAR SIGNALING; POST-SYNAPTIC POTENTIALS; STRUCTURAL ORGANIZATION; SYNAPTIC STRENGTHS;

COMPLEX NETWORKS;

EID: 84895741559     PISSN: 10495258     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (21)

1. J. J. Hopfield, "Neural networks and physical systems with emergent collective computational abilities, " Proc. Natl. Acad. Sci. U.S.A. 79 (1982) no. 8, 2554-2558.
2. D. J. Amit, H. Gutfreund, and H. Sompolinsky, "Spin-glass models of neural networks, " Phys. Rev. A 32 (Aug, 1985) 1007-1018.
3. E. Gardner, "The space of interactions in neural network models, " Journal of Physics A: Mathematical and General 21 (1988) no. 1, 257.
4. T. V. P. Bliss and G. L. Collingridge, "A synaptic model of memory: long-term potentiation in the hippocampus, " Nature 361 (Jan, 1993) 31-39.
5. C. C. H. Petersen, R. C. Malenka, R. A. Nicoll, and J. J. Hopfield, "All-or-none potentiation at CA3-CA1 synapses, " Proc. Natl. Acad. Sci. U.S.A. 95 (1998) no. 8, 4732-4737.
6. D. H. O'Connor, G. M. Wittenberg, and S. S.-H. Wang, "Graded bidirectional synaptic plasticity is composed of switch-like unitary events, " Proc. Natl. Acad. Sci. U.S.A. 102 (2005) no. 27, 9679-9684.
7. R. Enoki, Y. ling Hu, D. Hamilton, and A. Fine, "Expression of Long-Term Plasticity at Individual Synapses in Hippocampus Is Graded, Bidirectional, and Mainly Presynaptic: Optical Quantal Analysis, " Neuron 62 (2009) no. 2, 242-253.
8. D. J. Amit and S. Fusi, "Constraints on learning in dynamic synapses, " Network: Computation in Neural Systems 3 (1992) no. 4, 443-464.
9. D. J. Amit and S. Fusi, "Learning in neural networks with material synapses, " Neural Computation 6 (1994) no. 5, 957-982.
10. S. Fusi, P. J. Drew, and L. F. Abbott, "Cascade models of synaptically stored memories, " Neuron 45 (Feb, 2005) 599-611.
11. S. Fusi and L. F. Abbott, "Limits on the memory storage capacity of bounded synapses, " Nat. Neurosci. 10 (Apr, 2007) 485-493.
12. C. Leibold and R. Kempter, "Sparseness Constrains the Prolongation of Memory Lifetime via Synaptic Metaplasticity, " Cerebral Cortex 18 (2008) no. 1, 67-77.
13. D. S. Bredt and R. A. Nicoll, "AMPA Receptor Trafficking at Excitatory Synapses, " Neuron 40 (2003) no. 2, 361-379.
14. M. P. Coba, A. J. Pocklington, M. O. Collins, M. V. Kopanitsa, R. T. Uren, S. Swamy, M. D. Croning, J. S. Choudhary, and S. G. Grant, "Neurotransmitters drive combinatorial multistate postsynaptic density networks, " Sci Signal 2 (2009) no. 68, ra19.
15. W. C. Abraham and M. F. Bear, "Metaplasticity: the plasticity of synaptic plasticity, " Trends in Neurosciences 19 (1996) no. 4, 126-130.
16. J. M. Montgomery and D. V. Madison, "State-Dependent Heterogeneity in Synaptic Depression between Pyramidal Cell Pairs, " Neuron 33 (2002) no. 5, 765-777.
17. R. D. Emes and S. G. Grant, "Evolution of Synapse Complexity and Diversity, " Annual Review of Neuroscience 35 (2012) no. 1, 111-131.
18. A. B. Barrett and M. C. van Rossum, "Optimal learning rules for discrete synapses, " PLoS Comput. Biol. 4 (Nov, 2008) e1000230.
19. J. Kemeny and J. Snell, Finite markov chains. Springer, 1960.
20. C. Burke and M. Rosenblatt, "A Markovian function of a Markov chain, " The Annals of Mathematical Statistics 29 (1958) no. 4, 1112-1122.
21. F. Ball and G. F. Yeo, "Lumpability and Marginalisability for Continuous-Time Markov Chains, " Journal of Applied Probability 30 (1993) no. 3, 518-528.