Learning and replaying spatiotemporal sequences: A replication study

Frontiers in Integrative Neuroscience, vol. 16, pp. 974177

Abstract

Learning and replaying spatiotemporal sequences are fundamental computations performed by the brain and specifically the neocortex. These features are 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, a recent study 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. For the model to serve as the basis for further research, it is important to determine its replicability. In this Brief Report, we give a detailed description of the model and identify 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.

Authors 3

1. Jette Oberländer Aachen Department of Computer Science 3-Software Engineering

   RWTH Aachen University · Forschungszentrum Jülich · Jülich Aachen Research Alliance

   Affiliation as printed

   Department of Computer Science 3-Software Engineering, RWTH Aachen University, Aachen, Germany

   Institute of Neuroscience and Medicine (INM-6), Institute for Advanced Simulation (IAS-6), JARA-Institute Brain Structure-Function Relationship (JBI-1/INM-10), Research Centre Jülich, Jülich, Germany

   Department of Computer Science 3-Software Engineering, RWTH Aachen University, Germany

   Institute of Neuroscience and Medicine (INM-6), Institute for Advanced Simulation (IAS-6), JARA-Institute Brain Structure-Function Relationship (JBI-1/INM-10), Research Centre Jülich, Germany

2. Younes Bouhadjar corresponding Aachen

   RWTH Aachen University · Forschungszentrum Jülich · Jülich Aachen Research Alliance

   Affiliation as printed

   Institute of Neuroscience and Medicine (INM-6), Institute for Advanced Simulation (IAS-6), JARA-Institute Brain Structure-Function Relationship (JBI-1/INM-10), Research Centre Jülich, Jülich, Germany

   Jülich Research Centre and JARA, Peter Grünberg Institute (PGI-7, 10), Jülich, Germany

   RWTH Aachen University, Aachen, Germany

   Institute of Neuroscience and Medicine (INM-6), Institute for Advanced Simulation (IAS-6), JARA-Institute Brain Structure-Function Relationship (JBI-1/INM-10), Research Centre Jülich, Germany

   Jülich Research Centre and JARA, Peter Grünberg Institute (PGI-7, 10), Germany

   RWTH Aachen University, Germany

3. Abigail Morrison Aachen Department of Computer Science 3-Software Engineering

   RWTH Aachen University · Forschungszentrum Jülich · Jülich Aachen Research Alliance

   Affiliation as printed

   Department of Computer Science 3-Software Engineering, RWTH Aachen University, Aachen, Germany

   Institute of Neuroscience and Medicine (INM-6), Institute for Advanced Simulation (IAS-6), JARA-Institute Brain Structure-Function Relationship (JBI-1/INM-10), Research Centre Jülich, Jülich, Germany

   Department of Computer Science 3-Software Engineering, RWTH Aachen University, Germany

   Institute of Neuroscience and Medicine (INM-6), Institute for Advanced Simulation (IAS-6), JARA-Institute Brain Structure-Function Relationship (JBI-1/INM-10), Research Centre Jülich, Germany