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The pruning that is associated with learning is known as small-scale axon terminal arbor pruning. Axons extend short axon terminal arbors toward neurons within a target area. Certain terminal arbors are pruned by competition. The selection of the pruned terminal arbors follow the "use it or lose it" principle seen in synaptic plasticity. This ...
Neuroplasticity, also known as neural plasticity or just plasticity, is the ability of neural networks in the brain to change through growth and reorganization. Neuroplasticity refers to the brain's ability to reorganize and rewire its neural connections, enabling it to adapt and function in ways that differ from its prior state.
Activity-dependent plasticity is a form of functional and structural neuroplasticity that arises from the use of cognitive functions and personal experience. [ 1 ] Hence, it is the biological basis for learning and the formation of new memories .
Spike-Timing-Dependent Plasticity (STDP), for example, refines Hebbian principles by incorporating the precise timing of neuronal spikes. Experimental advancements have also linked Hebbian learning to complex behaviors, such as decision-making and emotional regulation, showcasing its versatility.
Neural plasticity refers to any change in the structure of the neural network that forms the central nervous system. Neural plasticity is the neuronal basis for changes in how the mind works, including learning, the formation of memory, and changes in intelligence. One well-studied form of plasticity is Long-Term Potentiation (LTP). [6]
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] Since memories are postulated to be represented by vastly interconnected neural circuits in the brain , synaptic plasticity is one of the important neurochemical foundations of learning ...
The underlying principle of synaptic plasticity is that synapses undergo an activity-dependent and selective strengthening or weakening so that new information can be stored. [ 4 ] [ 5 ] Synaptic plasticity depends on numerous factors including the threshold of the presynaptic stimulus in addition to the relative concentrations of ...
The basic BCM rule takes the form = (()) (),where: is the synaptic weight of the th synapse,; is th synapse's input current, = () = () is the inner product of weights and input currents (weighted sum of inputs),