Spike timing-dependent plasticity (STDP) plays an important role in sculpting neural circuits to store information in the hippocampus, since motor learning and memory are thought to be closely linked with this type of synaptic plasticity. We built a computational model to study the potential learning rule by linearly changing the synaptic weight and number of the synapses involved. The main findings are the following: (i) changes in the synaptic weight and number of synapses can lead to different long-term changes in the synaptic efficacy; (ii) the first spike pair of two neurons exerts a great influence on the subsequent spike pair; a pre-post spiking pair reinforces the subsequent paired spiking, while a post-pre spiking pair depresses this paired spiking; (iii) when the synaptic weight and synaptic number change, the interval in the first spiking pair is reduced, which directly influences the first spiking pair, and (iv) when a stellate neuron is stimulated weakly or the capacitance of a CA1 pyramidal neuron is decreased, LTP is produced more easily than LTD; in the opposite case, LTD is produced more readily; an increase of the synaptic number can promote activation of CA1 pyramidal neurons.
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Ren, H., Liu, S.Q., Zhang, X. et al. Spike Timing-Dependent Plasticity in CA1 Pyramidal Neuron-Controlling Hippocampal Circuits: a Model Study. Neurophysiology 46, 300–307 (2014). https://doi.org/10.1007/s11062-014-9448-z
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DOI: https://doi.org/10.1007/s11062-014-9448-z