A model-based prediction of the calcium responses in the striatal synaptic spines depending on the timing of cortical and dopaminergic inputs and post-synaptic spikes

Abstract

The dopamine-dependent plasticity of the cortico-striatal synapses is considered as the cellular mechanism crucial for reinforcement learning. The dopaminergic inputs and the calcium responses affect the synaptic plasticity by way of the signaling cascades within the synaptic spines. The calcium concentration within synaptic spines, however, is dependent on multiple factors including the calcium influx through ionotropic glutamate receptors, the intracellular calcium release by activation of metabotropic glutamate receptors, and the opening of calcium channels by EPSPs and back-propagating action potentials. Furthermore, dopamine is known to modulate the efficacies of NMDA receptors, some of the calcium channels, and sodium and potassium channels that affect the back propagation of action potentials. Here we construct an electric compartment model of the striatal medium spiny neuron with a realistic morphology and predict the calcium responses in the synaptic spines with variable timings of the glutamatergic and dopaminergic inputs and the postsynaptic action potentials. The model was validated by reproducing the responses to current inputs and could predict the electric and calcium responses to glutamatergic inputs and back-propagating action potential in the proximal and distal synaptic spines during up- and down-states. We investigated the calcium responses by systematically varying the timings of the glutamatergic and dopaminergic inputs relative to the action potential and found that the calcium response and the subsequent synaptic potentiation is maximal when the dopamine input precedes glutamate input and action potential. The prediction is not consistent with the hypothesis that the dopamine input provides the reward prediction error for reinforcement learning. The finding suggests that there is an unknown learning mechanisms at the network level or an unknown cellular mechanism for calcium dynamics and signaling cascades.

Signaling models for dopamine-dependent temporal contiguity in striatal synaptic plasticity

“…The wider time window is mainly due to the slower dynamics of G-protein and Ca 2+ signals, e.g., their activation time constants are 7 s and 10 s, respectively, but further studies are necessary to clarify such species-dependent difference in RP signaling. Nakano et al have also developed a model of RP [ 12 ]. They considered acute DA effects on NMDARs and VGCCs within several hundred milliseconds, and predicted larger Ca 2+ signals when the DA signal precedes postsynaptic spiking.…”

“…Thus, two signaling models based on AC5 have been proposed to explain how those signaling dynamics result in timing dependence. One explores the direct effect of DA signal on NMDARs and VGCCs [12], and the other models DA-delay dependence in Ca 2+ dynamics through a Ca 2+ buffer [13]. However, those models predict 100-ms and 20-s time windows, respectively, and it still remains obscure about the mechanisms of the in-between time window of~2 s, which is required for DA-mediated reward conditioning [14].…”

A critical time window for dopamine actions on the structural plasticity of dendritic spines

“…Animals learn to associate the sensorimotor signals with subsequent rewards through reinforcement of the neuronal circuits involving dopamine ( 5 - 7 ). Despite its importance, this narrow timing detection has never been demonstrated at the cellular level and might be ascribed to neural network properties ( 6 , 8 ).…”

Ca2+ dysregulation in the endoplasmic reticulum related to Alzheimer’s disease: A review on experimental progress and computational modeling

“…For example, by using Chemesis, a neural simulation software, Blackwell and Kotaleski demonstrated that the involvement of RyR channels affects the model simulation behavior of Ca 2+ wave propagation (Blackwell and Kotaleski, 2003). The models, which included RyR-regulated Ca 2+ signaling, have considered that both RyR and IP 3 R contribute to Ca 2+ release from the ER and the CICR is governed by RyR (de Schutter and Smolen, 1998;Nakano et al, 2013). Similar strategies to IP 3 R modeling have been applied in RyR models.…”