摘要：Abstract Background We are interested in an asynchronous graph based model, G ( N , E ) $\boldsymbol {\mathcal {G}(N,E)}$ of cognition or cognitive dysfunction, where the nodes N provide computation at the neuron level and the edges E i→j between nodes N i and node N j specify internode calculation. Methods We discuss how to improve update and evaluation needs for fast calculation using approximations of neural processing for first and second messenger systems as well as the axonal pulse of a neuron. Results These approximations give rise to a low memory footprint profile for implementation on multicore platforms using functional programming languages such as Erlang, Clojure and Haskell when we have no shared memory and all states are immutable. Conclusions The implementation of cognitive models using these tools on such platforms will allow the possibility of fully realizable lesion and longitudinal studies.
关键词：Cognition models;Graphs of computational nodes;Nodal computation approximation
