Biophysically plausible models of synaptic plasticity

Biophysically plausible models of synaptic plasticity Understanding Synaptic Plasticity Through Biophysically Plausible Models Synaptic plasticity—the ability of synapses to strengthen or weaken over time—is a cornerstone of learning and memory in the brain. To study this complex biological phenomenon, researchers are increasingly turning to biophysically plausible models that simulate how neural connections change based on activity, spike timing, and chemical signaling. Unlike abstract machine learning algorithms, these models incorporate detailed mechanisms such as ion channel dynamics, neurotransmitter diffusion, dendritic integration, and spike-timing-dependent plasticity (STDP). They help bridge the gap between cellular neuroscience and systems-level computation , offering insights into how real neurons learn and adapt. One prominent example is STDP-based modeling , where the timing of spikes from pre- and postsynaptic neurons determines whether synaptic strength is inc...