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Effects of short-term synaptic plasticity mechanisms on the dynamics of the network conductances

BMC Neuroscience201415 (Suppl 1) :P150

https://doi.org/10.1186/1471-2202-15-S1-P150

  • Published:

Keywords

  • Firing Rate
  • Inhibitory Neuron
  • Plasticity Mechanism
  • Saddle Node
  • Connectivity Rule

In this work, we analyzed the effects that different levels of short-term synaptic facilitation and depression cause on the dynamics of the conductances in a network of excitatory and inhibitory neurons.

For this purpose, we added the short-term plasticity mechanisms for depression and facilitation [2] to the biophysical network model described in [1]. This model is made up of a population of excitatory and inhibitory multi-compartment neurons containing different membrane channels modeled according to the Hodgkin-Huxley formalism. Neurons have been spatially arranged on a line to emulate the connectivity rule experimentally observed in visual cortex. The synaptic transmission has been mediated by excitatory AMPA and NMDA, and inhibitory GABA currents.

Depending on the level of depression, the dynamics of the network presents two different behaviors: (i) a regime of up and down states; (ii) tonic activity [3]. By plotting the firing rate of excitatory versus inhibitory neurons, our results show that, without depression, the model seems to present a periodic orbit closed to one saddle node and one attractor. The possible saddle node, which corresponds to the (0,0) point is reached and maintained for a couple of seconds by the spontaneous dynamics causing down states. On the contrary, with respect to the possible attractor, by increasing the level of depression, the trajectory turns around it inducing oscillations in the firing rate scenario. For these levels, the trajectory reaches the saddle causing the tonic firing. We defined as critical value the minimum level of depression that switch the network behavior.

The critical value of the depression plays an important role in the changes of the conductances. As the depression level decreases we observe that the excitatory and inhibitory conductances exponentially increase (the goodness of fit presents an R2 coefficient greater than 0.8) for all levels of depression which are not close to the critical value.

On the other hand, the network shows a very different dynamics under the presence of short-term facilitation. In this case, we observed that for a low probability of release the network is active only for few seconds with a low firing rate. By increasing the level of facilitation of the network, we increased the probability to achieve up states with higher firing rate. These two different behaviors could be explained by observing that for low values of facilitation the AMPA conductances are almost zero. No claims can be inferred in the conductance values by changing the facilitation level since the data cannot be fitted by any kind of curve.

Finally, both facilitation and depression were treated together to see the joint effects on the network dynamics.

Authors’ Affiliations

(1)
Department of Mathematics and Computer Science, Escola Politècnica Superior, Universitat de les Illes Balears, Mallorca, Palma, 07122, Spain
(2)
Department of Informatics, Bioengineering, Robotics, System Engineering (DIBRIS), University of Genova, Genova, Italy
(3)
Department of Applied Mathematics I, EPSEB, Universitat Politècnica de Catalunya, Barcelona, Spain

References

  1. Compte A, Sanchez-Vives MV, McCormick DA, Wang X-J: Cellular and Network Mechanisms of Slow Oscillatory Activity (<1 Hz) and Wave Propagations in a Cortical Network Model. J. Neurophysiol. 2003, 89: 2707-2725. 10.1152/jn.00845.2002.View ArticlePubMedGoogle Scholar
  2. Dayan P, Abbott LF: Theoretical Neuroscience. The MIT Press. 2005Google Scholar
  3. Benita JM, Guillamon A, Deco G, Sanchez-Vives MV: Synaptic depression and slow oscillatory activity in a biophysical network model of the cerebral cortex. Front. Comput. Neurosci. 2012, 6: 64-PubMed CentralView ArticlePubMedGoogle Scholar

Copyright

© Vich et al; licensee BioMed Central Ltd. 2014

This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

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