Learning intentional behavior in the K-model of the amygdala and entorhinal cortex with the cortico-hyppocampal formation

Neurocomputing

Volumn 65-66, Issue SPEC. ISS., 2005, Pages 23-30

  Kozma, Robert ^a   Wong, Derek ^a   Demirer, Murat ^b   Freeman, Walter J ^c  

^a UNIVERSITY OF MEMPHIS   (United States)

^b University of Memphis   (United States)

^c UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

Amygdala; Entorhinal cortex; KIV model; Phase transition; Spatial EEG

Indexed keywords

BRAIN; DECISION MAKING; LEARNING SYSTEMS; MATHEMATICAL MODELS; PHASE TRANSITIONS;

K-MODEL; MACROSCOPIC PHASE TRANSITIONS; SENSORY CLUES; VERTEBRATE BRAINS;

NEUROLOGY;

AMYGDALOID NUCLEUS; ARTICLE; BEHAVIOR; BRAIN CORTEX; CALCULATION; COMPUTER SIMULATION; CONTROLLED STUDY; DECISION MAKING; ELECTROENCEPHALOGRAM; ENTORHINAL CORTEX; HIPPOCAMPUS; LEARNING; MATHEMATICAL MODEL; MOTIVATION; MOTOR CONTROL; NONHUMAN; ORIENTATION; PHASE TRANSITION; PRIORITY JOURNAL; REINFORCEMENT; SENSORY SYSTEM; VERTEBRATE;

EID: 18144392730     PISSN: 09252312     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.neucom.2004.10.106     Document Type: Article

References (11)

1. A. Bechara H. Damasio A.R. Damasio Emotion, decision making and the orbitofrontal cortex Cerebral Cortex 10 2000 295-307
2. H.J. Chang W.J. Freeman Parameter optimization in models of the olfactory system Neural Networks 9 1996 1-14
3. W.J. Freeman Mass Action in the Nervous System 1975 Academic Press New York
4. W.J. Freeman B.C. Burke M.D. Holmes Aperiodic phase resetting in scalp EEG of beta-gamma oscillations by state transitions at alpha-theta rates Human Brain Mapp. 19 2003 248-272
5. R. Kozma W.J. Freeman Chaotic resonance: Methods and applications for robust classification of noisy and variable patterns Int. J. Bifurcat. Chaos 11 6 2001 2307-2322
6. R. Kozma W.J. Freeman Basic principles of the KIV model and its application to the navigation problem J. Integr. Neurosci. 2 2003 125-145
7. R. Kozma W.J. Freeman P. Erdi The KIV model-nonlinear spatio-temporal dynamics of the primordial vertebrate forebrain Neurocomputing 55-56 2003 819-826
8. J.E. LeDoux Orbitofrontal cortex and basolateral amygdala encode expected outcomes during learning Annu. Rev. Neurosci. 23 2000 155-184
9. G. Schoenbaum A.A. Chiba M. Gallagher Neural encoding in orbitofrontal cortex and basolateral amygdala during olfactory discrimination learning J. Neurosci. 19 2000 1876-1884
10. D. Wong R. Kozma E. Tunstel W.J. Freeman Navigation in a challenging martian environment using multi-sensory fusion in KIV model, Proceedings of the International Conference on Robotics and Automation ICRA'04, vol. 1, New Orleans, pp. 672-677
11. W. Zhou R. Coggins Computational models of the amygdala and the orbitofrontal cortex: A hierarchical reinforcement learning system for robotic control, in: R.I. McKay, J. Slaney (Eds.), Lecture Notes AI: LNAI 2557, pp. 419-430