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The term long-term potentiation comes from the fact that this increase in synaptic strength, or potentiation, lasts a very long time compared to other processes that affect synaptic strength. [ 1 ] In neuroscience , long-term potentiation ( LTP ) is a persistent strengthening of synapses based on recent patterns of activity.
Homosynaptic plasticity is one type of synaptic plasticity. [1] Homosynaptic plasticity is input-specific, meaning changes in synapse strength occur only at post-synaptic targets specifically stimulated by a pre-synaptic target. [2] Therefore, the spread of the signal from the pre-synaptic cell is localized.
The changes in synaptic weight that occur is known as synaptic plasticity, and the process behind long-term changes (long-term potentiation and depression) is still poorly understood. Hebb's original learning rule was originally applied to biological systems, but has had to undergo many modifications as a number of theoretical and experimental ...
It can result from strong synaptic stimulation (as occurs in the cerebellar Purkinje cells) or from persistent weak synaptic stimulation (as in the hippocampus). Long-term potentiation (LTP) is the opposing process to LTD; it is the long-lasting increase of synaptic strength. In conjunction, LTD and LTP are factors affecting neuronal synaptic ...
A synaptic potential may get stronger or weaker over time, depending on a few factors. The quantity of neurotransmitters released can play a large role in the future strength of that synapse's potential. Additionally, the receptors on the post-synaptic side also play a role, both in their numbers, composition, and physical orientation.
According to the BCM model, when a pre-synaptic neuron fires, the post-synaptic neurons will tend to undergo LTP if it is in a high-activity state (e.g., is firing at high frequency, and/or has high internal calcium concentrations), or LTD if it is in a lower-activity state (e.g., firing in low frequency, low internal calcium concentrations). [1]
Synaptic plasticity rule for gradient estimation by dynamic perturbation of conductances. In neuroscience, synaptic plasticity is the ability of synapses to strengthen or weaken over time, in response to increases or decreases in their activity. [1]
In 1973, M. M. Taylor [1] suggested that if synapses were strengthened for which a presynaptic spike occurred just before a postsynaptic spike more often than the reverse (Hebbian learning), while with the opposite timing or in the absence of a closely timed presynaptic spike, synapses were weakened (anti-Hebbian learning), the result would be an informationally efficient recoding of input ...