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Dopamine modulated dynamical changes in recurrent networks with short term plasticity

Dopamine is commonly considered as reward signal [1] and also as punishment signal [2] and is tightly coupled with memory and learning processes. In computational models, learning is simulated often on synapses by spike-timing-dependent plasticity (STDP), which depends on fine-timescale relationships between pre and postsynaptic spikes. However, most neocortical synapses exhibit also a mixture of depression and facilitation in a short time scale of few hundred milliseconds, which is referred as short-term plasticity [3, 4]. The short-term plasticity stabilizes the network activity and changes dramatically the network dynamics, up to evidences of behavior dependency [5].

In our modeling study, we investigate the dynamic changes in a recurrent, spiking neural network model at different dopamine levels and its interaction with the short-term plasticity. The network consists of excitatory and inhibitory biologically plausible neurons [6]. The network was established by local and long-range (displaced) connections [7] with GABAA, GABAB, NMDA and AMPA synapses. The synaptic efficiency (short-term plasticity) is modeled with the phenomenological model in [8]. The values and statistical distributions are taken from [8, 9]. The influence of dopamine is approximated by up and down regulating of the maximal conductance of the GABAA and NMDA synapses on excitatory cells in same direction [1012].

We analyze STDP relevant events (pairs of pre- and postsynaptic spikes) in a range of -20...+20 ms on each synapse and found a clear dependency on dopamine level. Up regulating the conductance increases the number of such events and changes the distribution of the time differences. We demonstrate the effects of dopamine over a large variation of initial synaptic weights and stimulation patterns.

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Acknowledgements

Supported by the Deutsche Forschungsgemeinschaft (SFB 779), Saxony-Anhalt FKZ XN3590C/0305M and BMBF Bernstein Group Magdeburg.

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Correspondence to Andreas Herzog.

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Open Access This article is published under license to BioMed Central Ltd. This is an Open Access article is distributed under the terms of the Creative Commons Attribution 2.0 International License (https://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Herzog, A., Handrich, S. & Herrmann, C.S. Dopamine modulated dynamical changes in recurrent networks with short term plasticity. BMC Neurosci 10, P261 (2009). https://doi.org/10.1186/1471-2202-10-S1-P261

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Keywords

  • Dopamine
  • Neural Network Model
  • Short Time Scale
  • Dopamine Level
  • Synaptic Weight