Nodal computation approximations in asynchronous cognitive models

We are interested in an asynchronous graph based model, 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. 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. 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. The implementation of cognitive models using these tools on such platforms will allow the possibility of fully realizable lesion and longitudinal studies.