In this study, CMAC (Cerebellar ModelArticulated Controller) neural architectures are shown to be viable for the purposes of real-time learning and control. An adaptive critic neuro-control design has been implemented that learns in real-time how to back up a trailer truck along a fixed straight line trajectory. The truck backer-upper experiment is a standard performance measure in the neural network literature, but previously the training of the controllers was done off-line. With the CMAC neural architectures, it was possible to train the neuro-controllers on-line in real-time on a MS-DOS PC 386.

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Background: 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. 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.

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