The proposal specified in this application is a continuation of the previous project aimed at building large scale neural network models. We continue the development and simulations of two major families of models – spiking neural networks for brain simulations and non-spiking network architectures of the working memory of the brain, including the close interaction between short-term memory in Prefrontal cortex and long-term memory in parieto-temporal cortical regions. The main focus is on memory modular attractor memory network models. We have developed large-scale simulation models mostly in NEST simulators and have validated different alternative implementations. We have also implemented our synaptic plasticity learning rule (BCPNN) in Nest. The main emphasis is on studying the dynamics of working memory related processes and cortical dynamics. This analysis can now be performed in close relationship with biological mesoscopic recordings obtained from our experimental collaborators. By incorporating synaptic learning into our models we opened up an opportunity to build larger cognitive architectures accounting also for the hierarchical architecture of perceptual processing streams in the brain as well as of sequence encoding and spatio-temporal feature extraction. This will pave the way for future more comprehensive investigations into functional and dynamical aspects of the brain’s holistic cortical memory system.
Most simulation cases are in need of a large number of cores with relatively low memory load per core. In selected instances, simulations may take up to 6-7 hours.
This project proposal will account for new developments in the realm of SeRC Brain-IT initiative.