Computational principles of synaptic memory consolidation

Nature Neuroscience

Volumn 19, Issue 12, 2016, Pages 1697-1706

  Benna, Marcus K ^a   Fusi, Stefano ^a  

^a Columbia University ^*  (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ARTICLE; HUMAN; INFORMATION RETRIEVAL; LONG TERM DEPRESSION; LONG TERM POTENTIATION; MATHEMATICAL ANALYSIS; MEMORY CONSOLIDATION; MENTAL PERFORMANCE; MESENCEPHALON; NERVE CELL PLASTICITY; NORMAL DISTRIBUTION; PRIORITY JOURNAL; PROBABILITY; SIGNAL NOISE RATIO; SQUARE ROOT SCALING; SYNAPSE; ANIMAL; BIOLOGICAL MODEL; COMPUTER SIMULATION; MEMORY; NERVE CELL; NERVE CELL NETWORK; PHYSIOLOGY;

ANIMALS; COMPUTER SIMULATION; MEMORY; MEMORY CONSOLIDATION; MODELS, NEUROLOGICAL; NERVE NET; NEURONAL PLASTICITY; NEURONS; SYNAPSES;

EID: 84989912892     PISSN: 10976256     EISSN: 15461726     Source Type: Journal    
DOI: 10.1038/nn.4401     Document Type: Article

References (47)

1. Kandel, E., Swartz, J., Jessel, T., Siegelbaum, S. & Hudspeth, A. J. Principles of Neural Science (McGraw Hill, 2013).
2. Bhalla, U. S. Molecular computation in neurons: a modeling perspective. Curr. Opin. Neurobiol. 25, 31-37 (2014).
3. Amit, D. J. & Fusi, S. Learning in neural networks with material synapses. Neural Comput. 6, 957-982 (1994).
4. Fusi, S. Hebbian spike-driven synaptic plasticity for learning patterns of mean firing rates. Biol. Cybern. 87, 459-470 (2002).
5. Fusi, S. & Abbott, L. F. Limits on the memory storage capacity of bounded synapses. Nat. Neurosci. 10, 485-493 (2007).
6. McCloskey, M. & Cohen, N. J. Catastrophic interference in connectionist networks: the sequential learning problem. Psychol. Learn. Motiv. 24, 109-164 (1989).
7. Carpenter, G. & Grossberg, S. Pattern Recognition by Self-Organizing Neural Networks (MIT Press, 1991).
8. McClelland, J. L., McNaughton, B. L. & O'Reilly, R. C. Why there are complementary learning systems in the hippocampus and neocortex: insights from the successes and failures of connectionist models of learning and memory. Psychol. Rev. 102, 419-457 (1995).
9. Fusi, S., Drew, P. J. & Abbott, L. F. Cascade models of synaptically stored memories. Neuron 45, 599-611 (2005).
10. Hopfield, J. J. Neural networks and physical systems with emergent collective computational abilities. Proc. Natl. Acad. Sci. USA 79, 2554-2558 (1982).
11. Roxin, A. & Fusi, S. Efficient partitioning of memory systems and its importance for memory consolidation. PLoS Comput. Biol. 9, e1003146 (2013).
12. Brady, T. F., Konkle, T., Alvarez, G. A. & Oliva, A. Visual long-term memory has a massive storage capacity for object details. Proc. Natl. Acad. Sci. USA 105, 14325-14329 (2008).
13. Wixted, J. T. & Ebbesen, E. B. On the form of forgetting. Psychol. Sci. 2, 409-415 (1991).
14. Wixted, J. T. & Ebbesen, E. B. Genuine power curves in forgetting: a quantitative analysis of individual subject forgetting functions. Mem. Cognit. 25, 731-739 (1997).
15. Abraham, W. C. Metaplasticity: tuning synapses and networks for plasticity. Nat. Rev. Neurosci. 9, 387 (2008).
16. Anderson, John R. Learning and Memory (Wiley, 1995).
17. Smolen, P., Zhang, Y. & Byrne, J. H. The right time to learn: mechanisms and optimization of spaced learning. Nat. Rev. Neurosci. 17, 77-88 (2016).
18. Wu, X. E. & Mel, B. W. Capacity-enhancing synaptic learning rules in a medial temporal lobe online learning model. Neuron 62, 31-41 (2009).
19. Parisi, G. A memory which forgets. J. Phys. A Math. Gen. 19, L617 (1986).
20. Lisman, J. E. A mechanism for memory storage insensitive to molecular turnover: a bistable autophosphorylating kinase. Proc. Natl. Acad. Sci. USA 82, 3055-3057 (1985).
21. Fusi, S., Annunziato, M., Badoni, D., Salamon, A. & Amit, D. J. Spike-driven synaptic plasticity: theory, simulation, VLSI implementation. Neural Comput. 12, 2227-2258 (2000).
22. Brader, J. M., Senn, W. & Fusi, S. Learning real-world stimuli in a neural network with spike-driven synaptic dynamics. Neural Comput. 19, 2881-2912 (2007).
23. Graupner, M. & Brunel, N. Calcium-based plasticity model explains sensitivity of synaptic changes to spike pattern, rate, and dendritic location. Proc. Natl. Acad. Sci. USA 109, 3991-3996 (2012).
24. Clopath, C., Ziegler, L., Vasilaki, E., Büsing, L. & Gerstner, W. Tag-trigger-consolidation: a model of early and late long-term-potentiation and depression. PLoS Comput. Biol. 4, e1000248 (2008).
25. Ziegler, L., Zenke, F., Kastner, D. B. & Gerstner, W. Synaptic consolidation: from synapses to behavioral modeling. J. Neurosci. 35, 1319-1334 (2015).
26. Shankar, G. M. et al. Amyloid-beta; protein dimers isolated directly from Alzheimer's brains impair synaptic plasticity and memory. Nat. Med. 14, 837-842 (2008).
27. O'Connor, D. H., Wittenberg, G. M. & Wang, S. S. Graded bidirectional synaptic plasticity is composed of switch-like unitary events. Proc. Natl. Acad. Sci. USA 102, 9679-9684 (2005).
28. Carew, T. J., Pinsker, H. M. & Kandel, E. R. Long-term habituation of a defensive withdrawal reflex in Aplysia. Science 175, 451-454 (1972).
29. Zhou, Q., Tao, H. W. & Poo, M. M. Reversal and stabilization of synaptic modifications in a developing visual system. Science 300, 1953-1957 (2003).
30. Emes, R. D. et al. Evolutionary expansion and anatomical specialization of synapse proteome complexity. Nat. Neurosci. 11, 799-806 (2008).
31. Lahiri, S. & Ganguli, S. A memory frontier for complex synapses. Adv. Neural Inf. Process. Syst. 26, 1034-1042 (2013).
32. Crick, F. Memory and molecular turnover. Nature 312, 101 (1984).
33. Miller, P., Zhabotinsky, A. M., Lisman, J. E. & Wang, X. J. The stability of a stochastic CaMKII switch: dependence on the number of enzyme molecules and protein turnover. PLoS Biol. 3, e107 (2005).
34. Si, K., Lindquist, S. & Kandel, E. R. A neuronal isoform of the Aplysia CPEB has prion-like properties. Cell 115, 879-891 (2003).
35. Shouval, H. Z. Clusters of interacting receptors can stabilize synaptic efficacies. Proc. Natl. Acad. Sci. USA 102, 14440-14445 (2005).
36. Ji, D. & Wilson, M. A. Coordinated memory replay in the visual cortex and hippocampus during sleep. Nat. Neurosci. 10, 100-107 (2007).
37. Peyrache, A., Khamassi, M., Benchenane, K., Wiener, S. I. & Battaglia, F. P. Replay of rule-learning related neural patterns in the prefrontal cortex during sleep. Nat. Neurosci. 12, 919-926 (2009).
38. Reymann, K. G. & Frey, J. U. The late maintenance of hippocampal LTP: requirements, phases, 'synaptic tagging', 'late-associativity' and implications. Neuropharmacology 52, 24-40 (2007).
39. Redondo, R. L. & Morris, R. G. Making memories last: the synaptic tagging and capture hypothesis. Nat. Rev. Neurosci. 12, 17-30 (2011).
40. Barrett, A. B., Billings, G. O., Morris, R. G. & van Rossum, M. C. State based model of long-term potentiation and synaptic tagging and capture. PLoS Comput. Biol. 5, e1000259 (2009).
41. Amit, D. Modeling Brain Function (Cambridge Univ. Press, 1989).
42. Krauth, W. & Mézard, M. Learning algorithms with optimal stability in neural networks. J. Phys. A Math. Gen. 20, L745 (1987).
43. Tsodyks, M. V. & Feigel'man, M. V. The enhanced storage capacity in neural networks with low activity level. Europhys. Lett. 6, 101-105 (1988).
44. Barak, O., Rigotti, M. & Fusi, S. The sparseness of mixed selectivity neurons controls the generalization-discrimination trade-off. J. Neurosci. 33, 3844-3856 (2013).
45. O'Kane, D. & Treves, A. Why the simplest notion of neocortex as an autoassociative memory would not work. Network 3, 379-384 (1992).
46. Roudi, Y. & Latham, P. E. A balanced memory network. PLoS Comput. Biol. 3, 1679-1700 (2007).
47. Savin, C., Dayan, P. & Lengyel, M. Optimal recall from bounded metaplastic synapses: predicting functional adaptations in hippocampal area CA3. PLoS Comput. Biol. 10, e1003489 (2014).