Abstract
In this chapter, simple ideas about how to use a chain of computational nodes to build models of neural processing is outline using the OCOS neural subcircuit in the cortex. The OCOS is modeled as a graph and the incident matrix of the graph is used to construct the graph based notions of a gradient and a Laplacian. Information flow through the graph is assumed to follow a cable like equation using the graph version of the Laplacian. This cable like equation can then be used to map inputs to output in a chain model. Finally, some comments are made about the estimated sizes of interesting cortical models that could be used in simulations.
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Peterson, J.K. (2016). Training the Model. In: BioInformation Processing. Cognitive Science and Technology. Springer, Singapore. https://doi.org/10.1007/978-981-287-871-7_15
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DOI: https://doi.org/10.1007/978-981-287-871-7_15
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