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This article is part of the supplement: Twentieth Annual Computational Neuroscience Meeting: CNS*2011

Open Access Poster presentation

A unified computational model of the genetic regulatory networks underlying synaptic, intrinsic and homeostatic plasticity

Daniel Bush* and Yaochu Jin

Author Affiliations

Department of Computing, University of Surrey, Guildford, Surrey, GU7 2XH, UK

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BMC Neuroscience 2011, 12(Suppl 1):P161  doi:10.1186/1471-2202-12-S1-P161


The electronic version of this article is the complete one and can be found online at: http://www.biomedcentral.com/1471-2202/12/S1/P161


Published:18 July 2011

© 2011 Bush and Jin; licensee BioMed Central Ltd.

This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Poster presentation

It is well established that the phenomena of synaptic, intrinsic and homeostatic plasticity are mediated – at least in part – by a multitude of activity-dependent gene transcription and translation processes [1-3]. Various isolated aspects of the complex genetic regulatory network (GRN) underlying these interconnected plasticity mechanisms have been examined previously in detailed computational models [4,5]. However, no study has yet taken an integrated, systems biology approach to examining the emergent dynamics of these interacting elements over longer timescales. Here, we present theoretical descriptions and kinetic models of the principle mechanisms responsible for synaptic and neuronal plasticity within a single simulated Hodgkin-Huxley neuron. We describe how intracellular Calcium dynamics and neural activity mediate synaptic tagging and capture (STC), bistable CaMKII auto-phosphorylation, nuclear CREB activation via multiple converging secondary messenger pathways, and the activity-dependent accumulation of immediate early genes (IEGs) controlling homeostatic plasticity. We then demonstrate that this unified model allows a wide range of experimental plasticity data to be replicated. Furthermore, we describe how this model can be used to examine the cell-wide and synapse-specific effects of various activity regimes and putative pharmacological manipulations on neural processing over short and long timescales. These include an examination of the interaction between intrinsic and synaptic plasticity, each dictated by the level of activated CREB; and the differences in functionality generated by STC under regimes of reduced protein synthesis [2,6]. Finally, we discuss how these processes might contribute to maintaining an appropriate regime for transient dynamics in putative cell assemblies within contemporary neural network models of cognitive processing [7,8].

References

  1. Alberini CM: Transcription factors in long-term memory and synaptic plasticity.

    Physiological Reviews 2009, 89:121-145. PubMed Abstract | Publisher Full Text OpenURL

  2. Benito E, Barco A: CREB’s control of intrinsic and synaptic plasticity: implications for CREB-dependent memory models.

    Trends in Neuroscience 2010, 33:230-240. Publisher Full Text OpenURL

  3. Shepherd JD, Rumbaugh G, Wu J, Chowdhury S, Plath N, Kuhl D, Huganir RL, Worley PF: Arc/Arg3.1 mediates homeostatic synaptic scaling of AMPA receptors.

    Neuron 2006, 52:475-484. PubMed Abstract | Publisher Full Text | PubMed Central Full Text OpenURL

  4. Castellani GC, Bazzani A, Cooper LN: Towards a microscopic model of bidirectional synaptic plasticity.

    PNAS 2009, 106:14091-14095. PubMed Abstract | Publisher Full Text | PubMed Central Full Text OpenURL

  5. Zhang Y, Smolen P, Baxter DA, Byrne JH: The sensitivity of consolidation and reconsolidation to inhibitors of protein synthesis.

    Learning and Memory 2010, 17:428-439. Publisher Full Text OpenURL

  6. Fonseca R, Nagerl UV, Morris RGM, Bonhoeffer T: Competing for memory: Hippocampal LTP under regimes of reduced protein synthesis.

    Neuron 2004, 44:1011-1020. PubMed Abstract | Publisher Full Text OpenURL

  7. Buonomano DV: A learning rule for the emergence of stable dynamics and timing in recurrent networks.

    Journal of Neurophysiology 2005, 94:2275-2283. PubMed Abstract | Publisher Full Text OpenURL

  8. Lazar A, Pipa G, Triesch J: SORN: a self-organizing recurrent neural network.

    Frontiers in Computational Neuroscience 2009, 3:23. PubMed Abstract | Publisher Full Text | PubMed Central Full Text OpenURL