Neural Field Theory of Corticothalamic Prediction With Control Systems Analysis

Neural field theory is used to model and analyze realistic corticothalamic responses to simple visual stimuli. This yields system transfer functions that embody key features in common with those of engineering control systems, which enables interpretation of brain dynamics in terms of data filters. In particular, these features assist in finding internal signals that represent input stimuli and their changes, which are exactly the types of quantities used in control systems to enable prediction of future input signals, and adjustment of gains which is argued to be the analog of attention in control theory. Corticothalamic dynamics are shown to be analogous to the classical proportional-integral-derivative (PID) filters that are widely used in engineering.

[1]  Karl J. Friston,et al.  Attention, Uncertainty, and Free-Energy , 2010, Front. Hum. Neurosci..

[2]  Peter N. Robinson,et al.  STEADY STATES AND GLOBAL DYNAMICS OF ELECTRICAL ACTIVITY IN THE CEREBRAL CORTEX , 1998 .

[3]  P. Robinson,et al.  Multiscale brain modelling , 2005, Philosophical Transactions of the Royal Society B: Biological Sciences.

[4]  R. Jindra.  Mass action in the nervous system W. J. Freeman, Academic Press, New York (1975), 489 pp., (hard covers). $34.50 , 1976, Neuroscience.

[5]  Karl J. Friston.  The free-energy principle: a unified brain theory? , 2010, Nature Reviews Neuroscience.

[6]  Sacha Jennifer van Albada,et al.  Neurophysiological changes with age probed by inverse modeling of EEG spectra , 2010, Clinical Neurophysiology.

[7]  Tai Sing Lee,et al.  Hierarchical Bayesian inference in the visual cortex. , 2003, Journal of the Optical Society of America. A, Optics, image science, and vision.

[8]  A. Pouget,et al.  Probabilistic brains: knowns and unknowns , 2013, Nature Neuroscience.

[9]  John R. Terry,et al.  A unifying explanation of primary generalized seizures through nonlinear brain modeling and bifurcation analysis. , 2006, Cerebral cortex.

[10]  H. Haken,et al.  Field Theory of Electromagnetic Brain Activity. , 1996, Physical review letters.

[11]  H Spekreijse,et al.  Contrast evoked responses in man. , 1973, Vision research.

[12]  J. Cowan,et al.  A mathematical theory of the functional dynamics of cortical and thalamic nervous tissue , 1973, Kybernetik.

[13]  J. Hawkins,et al.  On Intelligence , 2004 .

[14]  L. Garey.  Cortex: Statistics and Geometry of Neuronal Connectivity, 2nd edn. By V. BRAITENBERG and A. SCHÜZ. (Pp. xiii+249; 90 figures; ISBN 3 540 63816 4). Berlin: Springer. 1998. , 1999 .

[15]  Peter A. Robinson,et al.  Quantitative theory of driven nonlinear brain dynamics , 2012, NeuroImage.

[16]  Lei Yang,et al.  Physiologically based calculation of steady-state evoked potentials and cortical wave velocities , 2008, Biological Cybernetics.

[17]  C. Herrmann.  Human EEG responses to 1–100 Hz flicker: resonance phenomena in visual cortex and their potential correlation to cognitive phenomena , 2001, Experimental Brain Research.

[18]  P A Robinson,et al.  Estimation of multiscale neurophysiologic parameters by electroencephalographic means , 2004, Human brain mapping.

[19]  Donald L Rowe,et al.  Estimation of neurophysiological parameters from the waking EEG using a biophysical model of brain dynamics. , 2004, Journal of theoretical biology.

[20]  P. Robinson,et al.  Physiologically based arousal state estimation and dynamics , 2015, Journal of Neuroscience Methods.

[21]  P. Robinson,et al.  Spatially uniform and nonuniform analyses of electroencephalographic dynamics,with application to the topography of the alpha rhythm. , 2004, Physical review. E, Statistical, nonlinear, and soft matter physics.

[22]  Karl J. Friston,et al.  Active Inference: A Process Theory , 2017, Neural Computation.

[23]  Rufin VanRullen,et al.  Perceptual Echoes at 10 Hz in the Human Brain , 2012, Current Biology.

[24]  Karl J. Friston,et al.  Uncertainty in perception and the Hierarchical Gaussian Filter , 2014, Front. Hum. Neurosci..

[25]  A. Borst.  Seeing smells: imaging olfactory learning in bees , 1999, Nature Neuroscience.

[26]  Karl J. Friston,et al.  Observing the Observer (II): Deciding When to Decide , 2010, PloS one.

[27]  James J. Wright,et al.  Propagation and stability of waves of electrical activity in the cerebral cortex , 1997 .

[28]  Karl J. Friston,et al.  A Bayesian Foundation for Individual Learning Under Uncertainty , 2011, Front. Hum. Neurosci..

[29]  Karl J. Friston,et al.  Observing the Observer (I): Meta-Bayesian Models of Learning and Decision-Making , 2010, PloS one.

[30]  Karl J. Friston,et al.  A theory of cortical responses , 2005, Philosophical Transactions of the Royal Society B: Biological Sciences.

[31]  P A Robinson,et al.  Multistability in the corticothalamic system. , 2017, Journal of theoretical biology.

[32]  P. N. Paraskevopoulos,et al.  Modern Control Engineering , 2001 .

[33]  P. Robinson,et al.  Dynamics of large-scale brain activity in normal arousal states and epileptic seizures. , 2002, Physical review. E, Statistical, nonlinear, and soft matter physics.

[34]  Peter A. Robinson,et al.  Physiology-based modeling of cortical auditory evoked potentials , 2008, Biological Cybernetics.

[35]  Karl J. Friston,et al.  Canonical Microcircuits for Predictive Coding , 2012, Neuron.