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Dopamine modulated dynamical changes in recurrent networks with short term plasticity
BMC Neuroscience volume 10, Article number: P261 (2009)
Dopamine is commonly considered as reward signal  and also as punishment signal  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 .
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 . The network was established by local and long-range (displaced) connections  with GABAA, GABAB, NMDA and AMPA synapses. The synaptic efficiency (short-term plasticity) is modeled with the phenomenological model in . 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 [10–12].
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.
Schultz W: Multiple reward signals in the brain. Nat Rev Neurosci. 2000, 1: 199-207. 10.1038/35044563.
Ungless MA, Magill PJ, Bolam JP: Uniform inhibition of dopamine neurons in the ventral tegmental area by aversive stimuli. Science. 2004, 303: 2040-2042. 10.1126/science.1093360.
Abbott LF, Regehr WG: Synaptic computation. Nature. 2004, 431: 796-803. 10.1038/nature03010.
Sussillo D, Toyoizumi T, Maass W: Self-tuning of neural circuits through short-term synaptic plasticity. J Neurophysiol. 2007, 97: 4079-4095. 10.1152/jn.01357.2006.
Fujisawa S, Amarasingham A, Harrison MT, Buzsáki G: Behavior-dependent short-term assembly dynamics in the medial prefrontal cortex. Nat Neurosci. 2008, 11: 823-833. 10.1038/nn.2134.
Izhikevich EM, Gally JA, Edelman GM: Spike-timing dynamicsof neuronal groups. Cereb Cortex. 2004, 14: 933-944. 10.1093/cercor/bhh053.
Herzog A, Kube K, Michaelis B, de Lima AD, Voigt T: Displaced strategies optimize connectivity in neocortical networks. Neurocomputing. 2007, 70: 1121-1129. 10.1016/j.neucom.2006.11.016.
Markram H, Wang Y, Tsodyks M: Differential signaling via the same axon of neocortical pyramidal neurons. Proc Natl Acad Sci. 1998, 95: 5323-5328. 10.1073/pnas.95.9.5323.
Gupta A, Wang Y, Markram H: Organizing principles for a diversity of GABAergic interneurons and synapses in the neocortex. Science. 2000, 287: 273-278. 10.1126/science.287.5451.273.
Wang X, Zhong P, Yan Z: Dopamine D4 receptors modulate GABAergic signaling in pyramidal neurons of prefrontal cortex. J Neurosci. 2002, 22: 9185-9193.
Wang X, Zhong P, Gu Z, Yan Z: Regulation of NMDA receptors by dopamine D4 signaling in prefrontal cortex. J Neurosci. 2003, 23: 9852-9861.
Durstewitz D, Seamans JK: The computational role of dopamine D1 receptors in working memory. Neural Netw. 2002, 15: 561-572. 10.1016/S0893-6080(02)00049-7.
Supported by the Deutsche Forschungsgemeinschaft (SFB 779), Saxony-Anhalt FKZ XN3590C/0305M and BMBF Bernstein Group Magdeburg.
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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
- Neural Network Model
- Short Time Scale
- Dopamine Level
- Synaptic Weight