Learning and replaying spatiotemporal sequences: A replication study - Oberländer, Jette; Bouhadjar, Younes; Morrison, Abigail (Frontiers in Integrative Neuroscience, 2022)
Learning and replaying spatiotemporal sequences are fundamental computations performed by the brain and specifically the neocortex. This feature is critical for a wide variety of cognitive functions, including sensory perception and the execution of motor and language skills. Although several computational models demonstrate this capability, many are either hard to reconcile with biological findings or have limited functionality. To address this gap, \cite{Maes2020} recently proposed a biologically plausible model based on a spiking recurrent neural network supplemented with read-out neurons. After learning, the recurrent network develops precise switching dynamics by successively activating and deactivating small groups of neurons. The read-out neurons are trained to respond to particular groups and can thereby reproduce the learned sequence. If the model is to serve as the basis of further research, it is important to determine its replicability. In this Brief Report, we give a detailed description of the model, pointing out missing details, inconsistencies or errors in or between the original paper and its reference implementation. We re-implement the full model in the neural simulator NEST in conjunction with the NESTML modeling language and confirm the main findings of the original work.