Working memory is a key component of cognition. The neural mechanism behind WM were until recently hypothesized to depend on persistent recurrent activation, but focus has shifted towards the role of a short-term ‘memory’ in the activated synapses. We tested whether a cortical spiking neural network model with fast Hebbian synaptic plasticity could learn a multi-item WM task (word list learning). The model could indeed reproduce human cognitive phenomena, while being simultaneously compatible with experimental data on structure, connectivity, and neurophysiology of the underlying cortical tissue. These findings are directly relevant to the ongoing paradigm shift in the WM field.
Reference: Fiebig F, Lansner A. (2017) A Spiking Working Memory Model Based on Hebbian Short-Term Potentiation,J Neurosci. 2017 Jan 4;37(1):83-96. doi: 10.1523/JNEUROSCI.1989-16.2017
