Topic: synaptic weight modifications

Understanding the trade-off between plasticity and stability–how neural networks form new memories while avoiding runaway potentiation or fast overwriting of memories–is a fundamental problem in neuroscience. Previous theoretical studies that relied on non-plausible plasticity rules did not reproduce realistic synaptic weight distributions or neural activity statistics and predicted fast forgetting times. We revisit this problem by investigating the implications of a multiplicative calcium-based plasticity rule in a recurrent network. This rule was recently fitted to experiments done in physio