Analysis of phase transitions in KIV with amygdala during simulated navigation control

A biologically inspired dynamical neural network model called KIV is used in this work to design autonomous agents. The KIV set models the vertebrate limbic system. Previous studies indicated that KIV is able to provide a control algorithm for navigation and decision-making for autonomous mobile agents. In this work we use Hilbert transform to capture global synchronized spatio-temporal patterns of amplitude modulation in KIV. We identify phase transition in the simulated amygdala and show that it shares several important features of EEC signals.

[1]  Weidong Zhou,et al.  Computational Models of the Amygdala and the Orbitofrontal Cortex: A Hierarchical Reinforcement Learning System for Robotic Control , 2002, Australian Joint Conference on Artificial Intelligence.

[2]  J. Kelso,et al.  Cortical coordination dynamics and cognition , 2001, Trends in Cognitive Sciences.

[3]  S. Bressler.  Cortical Coordination Dynamics and the Disorganization Syndrome in Schizophrenia , 2003, Neuropsychopharmacology.

[4]  Péter Érdi,et al.  The KIV model - nonlinear spatio-temporal dynamics of the primordial vertebrate forebrain , 2003, Neurocomputing.

[5]  R. Traub,et al.  A mechanism for generation of long-range synchronous fast oscillations in the cortex , 1996, Nature.

[6]  S. Bressler,et al.  Synchronized activity in prefrontal cortex during anticipation of visuomotor processing , 2002, Neuroreport.

[7]  Edward Tunstel.  Ethology as an Inspiration for Adaptive Behavior Synthesis in Autonomous Planetary Rovers , 2001, Auton. Robots.

[8]  R Quian Quiroga,et al.  Performance of different synchronization measures in real data: a case study on electroencephalographic signals. , 2002, Physical review. E, Statistical, nonlinear, and soft matter physics.

[9]  D. Liley,et al.  Robust chaos in a model of the electroencephalogram: Implications for brain dynamics. , 2001, Chaos.

[10]  Joseph E LeDoux.  Emotion Circuits in the Brain , 2000 .

[11]  W. Freeman,et al.  How brains make chaos in order to make sense of the world , 1987, Behavioral and Brain Sciences.

[12]  Robert Kozma,et al.  Basic principles of the KIV model and its application to the navigation problem. , 2003, Journal of integrative neuroscience.

[13]  S. Bressler.  Understanding Cognition Through Large-Scale Cortical Networks , 2002 .

[14]  W. Freeman,et al.  Fine temporal resolution of analytic phase reveals episodic synchronization by state transitions in gamma EEGs. , 2002, Journal of neurophysiology.

[15]  Walter J. Freeman,et al.  Origin, structure, and role of background EEG activity. Part 3. Neural frame classification , 2005, Clinical Neurophysiology.

[16]  W. Freeman,et al.  Spatiotemporal analysis of prepyriform, visual, auditory, and somesthetic surface EEGs in trained rabbits. , 1996, Journal of neurophysiology.

[17]  W. Freeman,et al.  Aperiodic phase re‐setting in scalp EEG of beta–gamma oscillations by state transitions at alpha–theta rates , 2003, Human brain mapping.

[18]  Robert Kozma,et al.  Navigation in a challenging Martian environment using multi-sensory fusion in KIV model , 2004, IEEE International Conference on Robotics and Automation, 2004. Proceedings. ICRA '04. 2004.

[19]  G. Schoenbaum,et al.  Orbitofrontal cortex and basolateral amygdala encode expected outcomes during learning , 1998, Nature Neuroscience.

[20]  Robert Kozma,et al.  Chaotic Resonance - Methods and Applications for Robust Classification of noisy and Variable Patterns , 2001, Int. J. Bifurc. Chaos.

[21]  G. Ermentrout,et al.  Gamma rhythms and beta rhythms have different synchronization properties. , 2000, Proceedings of the National Academy of Sciences of the United States of America.

[22]  Harald Atmanspacher,et al.  Pragmatic information and dynamical instabilities in a multimode continuous-wave dye laser , 1990 .

[23]  Walter J. Freeman,et al.  Origin, structure, and role of background EEG activity. Part 1. Analytic amplitude , 2004, Clinical Neurophysiology.

[24]  Philippe Faure,et al.  Is there chaos in the brain? II. Experimental evidence and related models. , 2003, Comptes rendus biologies.

[25]  W. Freeman,et al.  Change in pattern of ongoing cortical activity with auditory category learning , 2001, Nature.

[26]  F. Varela,et al.  Measuring phase synchrony in brain signals , 1999, Human brain mapping.

[27]  D. Liley,et al.  Theoretical electroencephalogram stationary spectrum for a white-noise-driven cortex: evidence for a general anesthetic-induced phase transition. , 1999, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.

[28]  R. Traub,et al.  Neuronal fast oscillations as a target site for psychoactive drugs. , 2000, Pharmacology & therapeutics.

[29]  Walter J. Freeman,et al.  Parameter optimization in models of the olfactory neural system , 1996, Neural Networks.

[30]  J. Martinerie,et al.  Comparison of Hilbert transform and wavelet methods for the analysis of neuronal synchrony , 2001, Journal of Neuroscience Methods.

[31]  Hans Liljenström,et al.  Disorder Versus Order in Brain Function - AN Introduction , 2000 .

[32]  W. Ditto,et al.  Controlling chaos in the brain , 1994, Nature.

[33]  A. Damasio,et al.  Emotion, decision making and the orbitofrontal cortex. , 2000, Cerebral cortex.

[34]  Frank W. Ohl,et al.  Early and late patterns of stimulus-related activity in auditory cortex of trained animals , 2003, Biological Cybernetics.