Sentence examples for changes of synaptic from inspiring English sources

Exact(19)

Numerous studies have suggested that the changes of synaptic efficacies at PF→PC (e.g., [40]) and also PF→IN [38] are important for adaptive cerebellar learning.

The latter process, but not the innate rewarding response, requires long-term changes of synaptic strength in VTA dopamine circuitry [3], [46].

Mosaic expression of fluorescent proteins in the CNS is essential for studying dynamic changes of synaptic connectivity using live imaging techniques.

One important conclusion of this very long IO lesion simulation is that the model can generate almost the same time course of the change in PC tonic activity without any changes of synaptic efficacy at PF→PC or PC→IN.

Ample evidence supports activity- or experience-dependent long-term changes of synaptic strength, i.e., long-term potentiation (LTP) and long-term depression (LTD), as the primary cellular mechanisms underlying multiple forms of learning and memory [5].

Biophysically, synaptic calcium transients are dependent on the morphology of the dendritic spine head, and therefore activity dependent changes of synaptic morphology are also likely to affect synaptic plasticity.

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Similar(40)

Meanwhile, the change of synaptic weights in STDP has a close relationship with the relative time of presynaptic/postsynaptic stimulus.

Another learning mechanism of biological synapses is spike-timing-dependent plasticity (STDP) [29], which implies that the change of synaptic weight is a strong function of the timing between the pre- and post-neuron spikes.

Here the change of calcium in time is emphasized to prevent a prolonged change of calcium concentration from dominating the change of synaptic efficacy.

Application of the dynamic-clamp requires current injection via a whole-cell electrode and therefore simulates the activation of a point conductance and not an overall change of synaptic input.

It should be noted that here we only consider the homeostatic PSD plasticity, which is one type of rate-based learning rules, where the change of synaptic weights is only dependent on the firing activity.

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