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      Modelling the interaction of structural and synaptic plasticity

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      1 , 2 , , 1 , 2 , 1 , 2
      BMC Neuroscience
      BioMed Central
      Twenty Second Annual Computational Neuroscience Meeting: CNS*2013
      13-18 July 2013

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          Abstract

          Recent experiments show that learning is associated with structural changes in neural tissue [1,2]. The underlying mechanism, named structural plasticity, drives the formation of new synapses and the removal of existing ones on a timescale of days to weeks. This enlarges the potential for information storage in neuronal networks [3] and is, thus, important for understanding long-term memory formation. On shorter timescales (minutes to hours) another process - synaptic plasticity - influences the transmission efficiency (weights) of a synapse and, therefore, also contributes to information storage. We investigate the interaction between these two processes - still widely unknown - in the following rather simple model: We use rate based neurons with the total transmission efficiency between two neurons being just the sum of weights of all synapses connecting these two neurons. Thus, the number of synapses as well as their weights influence the same quantity and we can investigate the effects arising from structural and synaptic plasticity competing on different timescales. Synaptic plasticity is modeled by Hebbian learning with weight-dependent synaptic scaling [4]. For structural plasticity we have to assume a certain number of potential synaptic sites. The formation of a synapse at each of these potential synaptic sites happens at random with a fixed formation probability. The removal of existing synapses also happens randomly, but with a probability, which depends on weight and postsynaptic activity. Although the interaction of these processes is quite complex, we show that the system converges to a stable state. In this state the activity determines the probability distribution of number and strength of synapses between neurons. This interplay could also serve to form highly interconnected clusters, which are candidates for memory representation (cell assemblies).

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          Cortical rewiring and information storage.

          Current thinking about long-term memory in the cortex is focused on changes in the strengths of connections between neurons. But ongoing structural plasticity in the adult brain, including synapse formation/elimination and remodelling of axons and dendrites, suggests that memory could also depend on learning-induced changes in the cortical 'wiring diagram'. Given that the cortex is sparsely connected, wiring plasticity could provide a substantial boost in storage capacity, although at a cost of more elaborate biological machinery and slower learning.
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            Synaptic Scaling in Combination with Many Generic Plasticity Mechanisms Stabilizes Circuit Connectivity

            Synaptic scaling is a slow process that modifies synapses, keeping the firing rate of neural circuits in specific regimes. Together with other processes, such as conventional synaptic plasticity in the form of long term depression and potentiation, synaptic scaling changes the synaptic patterns in a network, ensuring diverse, functionally relevant, stable, and input-dependent connectivity. How synaptic patterns are generated and stabilized, however, is largely unknown. Here we formally describe and analyze synaptic scaling based on results from experimental studies and demonstrate that the combination of different conventional plasticity mechanisms and synaptic scaling provides a powerful general framework for regulating network connectivity. In addition, we design several simple models that reproduce experimentally observed synaptic distributions as well as the observed synaptic modifications during sustained activity changes. These models predict that the combination of plasticity with scaling generates globally stable, input-controlled synaptic patterns, also in recurrent networks. Thus, in combination with other forms of plasticity, synaptic scaling can robustly yield neuronal circuits with high synaptic diversity, which potentially enables robust dynamic storage of complex activation patterns. This mechanism is even more pronounced when considering networks with a realistic degree of inhibition. Synaptic scaling combined with plasticity could thus be the basis for learning structured behavior even in initially random networks.
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              Author and article information

              Contributors
              Conference
              BMC Neurosci
              BMC Neurosci
              BMC Neuroscience
              BioMed Central
              1471-2202
              2013
              8 July 2013
              : 14
              : Suppl 1
              : P416
              Affiliations
              [1 ]Drittes Physikalisches Institut, Georg-Auguist Univeristät Göttingen, 37077, Germany
              [2 ]Bernstein Center for Computational Neuroscience Göttingen, Germany
              Article
              1471-2202-14-S1-P416
              10.1186/1471-2202-14-S1-P416
              3704660
              7eb26376-a407-432e-84b4-3b4d9c1bd77c
              Copyright ©2013 Fauth et al; 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.

              Twenty Second Annual Computational Neuroscience Meeting: CNS*2013
              Paris, France
              13-18 July 2013
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              Poster Presentation

              Neurosciences
              Neurosciences

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