Search results
Results From The WOW.Com Content Network
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]
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.
Synaptic tags mark where synaptic plasticity has occurred and can thus provide information on synaptic strength and potential for long-term plastic changes. [5] The tag is temporary and involves a large number of proteins, activated by the influx of Ca 2+ into the postsynaptic cell. [ 5 ]
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 ...
Hebbian theory is a neuropsychological theory claiming that an increase in synaptic efficacy arises from a presynaptic cell's repeated and persistent stimulation of a postsynaptic cell. It is an attempt to explain synaptic plasticity, the adaptation of brain neurons during the learning process.
In essence, it is the brain's ability to retain and develop memories based on activity-driven changes of synaptic strength that allow stronger learning of information. It is thought to be the growing and adapting quality of dendritic spines that provide the basis for synaptic plasticity connected to learning and memory. [19]
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 ...
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]