Regular Cycles of Forward and Backward Signal Propagation in Prefrontal Cortex and in Consciousness - Werbos, Paul J.; Davis, Joshua J. J. (Frontiers in Systems Neuroscience, 2016)
This paper addresses two fundamental questions: (1) Is it possible to develop mathematical neural network models which can explain and replicate the way in which higher-order capabilities like intelligence, consciousness, optimization and prediction emerge from the process of learning ?; and (2) How can we use and test such models in a practical way, to track, to analyze and to model high-frequency ( 500 hertz) many-channel data from recording the brain, just as econometrics sometimes uses models grounded in the theory of efficient markets to track real-world time-series data ? This paper first reviews some of the prior work addressing question (1), and then reports new work performed in MATLAB analyzing spike-sorted and burst-sorted data on the prefrontal cortex from the Buzsaki lab which is consistent with a regular clock cycle of about 153.4 milliseconds and with regular alternation between a forward pass of network calculations and a backwards pass, as in the general form of the backpropagation algorithm which one of us first developed in the period 1968-1974. In business and finance, it is well known that adjustments for cycles of the year are essential to accurate prediction of time-series data; in a similar way, methods for identifying and using regular clock cycles offer large new opportunities in neural time-series analysis. This paper demonstrates a few initial footprints on the large “continent” of this type of neural time-series analysis, and discusses a few of the many further possibilities opened up by this new approach to “decoding” the neural code. Three new general MATLAB functions and eight new numerical measures are discussed.