Topic: Hebbian synaptic plasticity

The importance of dendritic structures for neural computation has been recognized since the early days of neuroscience. However, it is only recently, with the increasing availability of data and advances in modelling techniques, that researchers have begun to map out the precise roles of different branching structures and synaptic distributions [1, 2]. Here, we investigate how the nonlinear integration of excitatory and inhibitory currents in dendritic branches can enhance the ability of a neuron to develop input selectivity and E/I balance via Hebbian synaptic plasticity. We model a single ra

Network models of cortex typically focus on excitatory population dynamics, relegating inhibition to a support role. However, recent studies have shown that memories can be encoded by both excitatory and inhibitory neurons (EI engrams), adding to the evidence of the functional importance of inhibitory neurons beyond mere stabilization. The functional consequence of such EI engrams is unclear, and the synaptic plasticity rules that could sustain them are unknown. Here, we explore in silico a mechanism for EI engrams storage and recall to glean possible computational benefits of such engrams ove