Changes in the efficacy of synaptic transmission in neural circuits are thought to underlie memory and learning. The traditional analogy is that of the synapse acting as a switch to retain a memory trace of the activity evoked by a particular experience. However, experience and neuronal activity are ongoing, and memory storage is a continuous process throughout life. Both experimental and theoretical work has shown that this ongoing activity and the plasticity it induces are highly destructive to memory traces maintained in synaptic efficacies. How can a synaptic memory trace be retained for a long time in the face of ongoing plasticity? Surprisingly, this question has received little theoretical attention. The solution proposed in this application is that synaptic plasticity does not act as a static, on-off switching mechanism, but rather as a complex, dynamic biochemical cascade. Preliminary models of such cascades indicate that they dramatically extend the amount of time over which memories can be retained. Cascade models provide a framework for understanding and accounting for the enormous complexity of the biochemical processes that underlie synaptic plasticity. The cascade approach to plasticity will be applied in network models to the problem of recognition memory and the response suppression that is its neural correlate. The proposed research will combine theoretical studies with both existing and new data to explore the implications of plasticity cascades for memory retention. Because these models account for the full range of dynamics exhibited by synaptic plasticity, they permit, for the first time, a detailed study of the dynamic aspects of memory, learning, and forgetting and their relationship to synaptic plasticity. The proposed research has important implications for the physiological basis of memory and its pathologies, and for our understanding of biochemical cascades and their role in shaping the dynamics of biological processes.

Agency
National Institute of Health (NIH)
Institute
National Institute of Mental Health (NIMH)
Type
Research Project (R01)
Project #
2R01MH058754-06
Application #
6823616
Study Section
Special Emphasis Panel (ZRG1-IFCN-E (02))
Program Officer
Glanzman, Dennis L
Project Start
1999-04-10
Project End
2009-03-31
Budget Start
2004-06-01
Budget End
2005-03-31
Support Year
6
Fiscal Year
2004
Total Cost
$239,172
Indirect Cost
Name
Brandeis University
Department
Miscellaneous
Type
Schools of Arts and Sciences
DUNS #
616845814
City
Waltham
State
MA
Country
United States
Zip Code
02454
Roxin, Alex; Fusi, Stefano (2013) Efficient partitioning of memory systems and its importance for memory consolidation. PLoS Comput Biol 9:e1003146
Monaco, Joseph D; Abbott, L F; Abbott, Larry F (2011) Modular realignment of entorhinal grid cell activity as a basis for hippocampal remapping. J Neurosci 31:9414-25
Rigotti, Mattia; Ben Dayan Rubin, Daniel; Morrison, Sara E et al. (2010) Attractor concretion as a mechanism for the formation of context representations. Neuroimage 52:833-47
Babadi, Baktash; Abbott, L F (2010) Intrinsic stability of temporally shifted spike-timing dependent plasticity. PLoS Comput Biol 6:e1000961
Sussillo, David; Abbott, L F (2009) Generating coherent patterns of activity from chaotic neural networks. Neuron 63:544-57
Monaco, Joseph D; Abbott, L F; Kahana, Michael J (2007) Lexico-semantic structure and the word-frequency effect in recognition memory. Learn Mem 14:204-13
Fusi, Stefano; Senn, Walter (2006) Eluding oblivion with smart stochastic selection of synaptic updates. Chaos 16:026112
Rumsey, Clifton C; Abbott, L F (2006) Synaptic democracy in active dendrites. J Neurophysiol 96:2307-18
Drew, Patrick J; Abbott, L F (2006) Extending the effects of spike-timing-dependent plasticity to behavioral timescales. Proc Natl Acad Sci U S A 103:8876-81
Drew, Patrick J; Abbott, L F (2006) Models and properties of power-law adaptation in neural systems. J Neurophysiol 96:826-33

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