Modeling long-term potentiation: Deterministic and stochastic approaches
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1
Tampere University of Technology, Finland
An essential feature of the functional brain is the synaptic plasticity in which long-term changes occur in the strength of connections (synapses) between neurons. Different forms of activity-dependent plasticity, for example long-term potentiation (LTP) and long-term depression (LTD), have been shown to exist. LTP and LTD are associated with long-term activity-dependent strengthening and weakening of synapses, respectively. Both LTP and LTD participate in storing information and inducing biochemical processes that ultimately lead to learning and memory. In this study, we focus on modeling the phenomena related to LTP.
LTP has been shown to have different phases. For example, Sweatt (1999) has proposed three sequentially occurring phases to explain LTP: short-term potentiation (STP; sometimes also called initial LTP, I-LTP), early LTP (E-LTP), and late LTP (L-LTP). I-LTP lasts approximately 30 min-45 min, E-LTP 30 min-3 h, and L-LTP several hours [1]. Each phase of the LTP (i.e., I-LTP, E-LTP, and L-LTP) involves three steps: induction, expression, and maintenance. Induction of LTP is a step where incoming action potentials are converted into biochemical signals and LTP begins. Maintenance corresponds to the persistent biochemical signals. These persistent biochemical signals can in turn activate receptors and result in LTP. Commonly studied phase, E-LTP, is induced when the concentration of calcium in a postsynaptic cell exceeds a specific threshold. The induction of E-LTP is characterized by transient protein kinase activation and the maintenance by autonomously active protein kinases. Furthermore, these autonomously active protein kinases can phosphorylate also AMPA receptors. Unlike E-LTP, L-LTP is found to require transcription and local dendritic protein synthesis of the postsynaptic cell [2].
In this study, different computational models for describing different phases and steps of LTP are studied and evaluated. Selected models, including both simplified and biophysically and chemically more detailed ones, are implemented, and their behavior is simulated with well-established deterministic and stochastic approaches, as well as with new stochastic methods [3]. Some of the models studied can mimic both the induction and maintenance of LTP, whereas others are found to explain only the induction. The ultimate goal of this work is to provide realistic, yet simple enough models for describing activity-dependent plasticity and addressing the general principles of information storage in neurons.
References
1. Sweatt JD. Towards a Molecular Explanation for Long-Term Potentiation. Learning & Memory 6:399-416, 1999.
2. Citri A and Malenka RC. Synaptic Plasticity: Multiple forms, Functions, and Mechanisms. Neuropsychopharmacoly Reviews 33:18-41, 2008.
3. Manninen T, Linne M-L, and Ruohonen K. Developing Ito stochastic differential equation models for neuronal signal transduction pathways. Computational Biology and Chemistry 30(4): 280-291, 2006
Conference:
Neuroinformatics 2008, Stockholm, Sweden, 7 Sep - 9 Sep, 2008.
Presentation Type:
Poster Presentation
Topic:
Computational Neuroscience
Citation:
Manninen
T,
Hituri
K and
Linne
M
(2008). Modeling long-term potentiation: Deterministic and stochastic approaches.
Front. Neuroinform.
Conference Abstract:
Neuroinformatics 2008.
doi: 10.3389/conf.neuro.11.2008.01.050
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Received:
28 Jul 2008;
Published Online:
28 Jul 2008.
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Correspondence:
Tiina Manninen, Tampere University of Technology, Tampere, Finland, tiina.h.manninen@gmail.com