Biophysical cell models have been central to understanding signal processing in brain cells and their networks, yet important limitations remain. First, the rich repertoire of nanoscale structures, such as dendritic spines and thin astrocyte processes, has been difficult to incorporate into whole-cell models because of...
L. Savtchenko, Sergey G. Aleksin, Chrysoula Tsimperi et al.· Nature Communications· 0 citations
BRAINCELL introduces a stochastic framework for generating populations of nanoscale morphological and physiological features grounded in empirical statistics, and enables task-specific predictions that often diverge from those produced by conventional models.
L. Savtchenko, Sergey G. Aleksin, D. Rusakov· bioRxiv· 0 citations
Neural-network simulations suggest that volume-transmitted excitatory signals can improve associative memory retrieval in sparsely connected networks and point to a potential computational consequence of glutamate spillover for brain circuit function.
O. Kopach, James P. Reynolds, Thomas P. Jensen et al.· bioRxiv· 0 citations
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