Methods in Neuronal Modeling: From Ions to Networks
Methods in Neuronal Modeling: From Ions to Networks
Dopamine-dependent plasticity of corticostriatal synapses
Neural Networks - Computational models of neuromodulation
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The striatum is the input nucleus of the basal ganglia and is thought to be involved in reinforcement learning. The striatum receives glutamate input from the cortex, which carries sensory information, and dopamine input from the substantia nigra, which carries reward information. Dopamine-dependent plasticity of cortico-striatal synapses is supposed to play a critical role in reinforcement learning. Recently, a number of labs reported contradictory results of its dependence on the timing of cortical inputs and spike output. To clarify the mechanisms behind spike timing-dependent plasticity of striatal synapses, we investigated spike timing-dependence of intracellular calcium concentration by constructing a striatal neuron model with realistic morphology. Our simulation predicted that the calcium transient will be maximal when cortical spike input and dopamine input precede the postsynaptic spike. The gain of the calcium transient is enhanced during the "up-state" of striatal cells and depends critically on NMDA receptor currents.