Fifty years ago, enthused by successes in creating digital computers and the DNA model of heredity, scientists were con?dent that solutions to the problems of und- standing biological intelligence and creating machine intelligence were within their grasp. Progress at ?rst seemed rapid. Giant 'brains' that ?lled air-conditioned rooms were shrunk into briefcases. The speed of computation doubled every two years. What these advances revealed is not the solutions but the dif?culties of the pr- lems. We are like the geographers who 'discovered' America, not as a collection of islands but as continents seen only at shores and demanding exploration. We are astounded less by the magnitude of our discoveries about how brains cogitate than by the enormity of the tasks we have undertaken, to explain and replicate the higher functions of brains. Five decades of brain research have led to the emergence of a new ?eld, which spans the entire range of brain cognition from quantum ?elds to social interactions, and which is combined by the conceptions of nonlinear neurodynamics operating simultaneously at and across all levels. A new breed of scientists has emerged, schooled in multiple academic disciplines, comfortable in working with data from different levels, and conversant with the mathematical tools that are essential to cross boundaries.

The latest progress in the field of cognitive neurodynamics Completely focused on the topic of cognitive neurodynamics Review and comments to the progress of cognitive neurodynamics by leading scientists in this field Covering almost all the sub-fields of cognitive neurodynamics

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Part I Microscopic Cognitive Neurodynamics. 1. Dynamic Interaction Networks and Global Ontology-Based Modelling of Brain Dynamics, Nikola Kasabov et. al.; 2. The Cauchy Problem for Spiking Neuron Models, Romain Brette. 3. On the Dynamics of Spectro-Temporal Tuning in Auditory Cortex, Didier A. Depireux et. al.; 4. A European Collaboration on Cerebellar LTD and Pattern Recognition, Erik De Schutter et. al.; 5. The Role of Layer 6 Feedback Cells in the Primary Visual Cortex, Wei Wang. 6. What Language is Spoken Here? Conversations Between Neurons in Primate Visual Cortex, Vivien A. Casagrande et. al.; 7. On the Emergence of Orientation Biases in V1, Mehdil and Nouri Shirazi. 8. Stress Affects Synaptic Plasticity and Basal Synaptic Transmission in the Rat Hippocampus In Vivo, Amer Kamal et. al.; 9. Physiological Evidence for Cantor Coding Output in Hippocampal CA1, Yasuhiro Fukushima et. al.; 10. Theta Phase Coding and Suppression of Irrelevant Plastic Change Through STDP in the Entorhino-Hippocampal System amid Background Noise, Jun Igarashi et. al.; 11. Effect of Low-Frequency Stimulation on Spontaneous Firing in Cultured Neuronal Networks, J. van Pelt et. al.; 12. Modeling Neurons of the Inferior Colliculus, Harry Erwin et. al.; 13. Synchronization Effects in Networks of Striatal Fast Spiking Interneurons Role of Gap Junctions, Johannes Hjorth et. al.; 14. Multilayer In-Place Learning Networks with Adaptive Lateral Connections: Models and Simulations, Weiya Shi et. al.; 15. Stimulus-Induced Pairwise Interaction Can Be Revealed by Information Geometric Approach, Hiroyuki Nakahara et. al.; 16. A Dynamical Model of Fast Intrinsic Optical Signal of Neural Burstings, Jianzhong Su et. al.; 17. The Mechanism of Bifurcation-Dependent Coherence Resonance of Morris-Lecar Neuron Model, Guangjun Zhang et. al.; 18. Noise-Induced Coherence Resonance in Morris-Lecar Neuron System, Bao-Hua et. al.; 19. Chaotic Burst Synchronization in a Small-World Neuronal Network, YanhongZheng et. al.; 20. A Stochastic Resonance Memory Mechanism of Hippocampus, Yan Chuankui et. al.; 21. Theta Phase Precession Enhance Single Trial Learning in an STDP Network, Enhua Shen et. al.; 22. A Numerical Mechanism for Square-Wave or Elliptic Bursting of Bursts in a Map-Based Neuron Network, Hongjun Cao et. al.; 23. Sub-Threshold Oscillation and Transient Response in Neural Coding, Jianxue Xu. 24. Setting Up New Memories: The Ideal Job for The Mammalian Dentate Gyrus; Gergely Papp and Alessandro Treves. 25. Neural Network Model Generating Symbol Sequence for Songs of Bengalese Finch, Junichirou Kotani et. al.; 26. A New Method for Characterizing the Variability of the Spike Trains, Ying Du et. al.; Part II Mesoscopic Cognitive Neurodynamics; Transitions Between Levels. 27. Proposed Renormalization Group Analysis of Nonlinear Brain Dynamics at Criticality, Walter J. Freeman et. al.; 28. Regulating Cortical Neurodynamics at Different Scales, Hans Liljenstrom. 29 A Thermodynamic Model of the Action-Perception Cycle in Brain Dynamics, Walter J. Freeman. 30. Dynamic Field Theory as the Interface Between Neuronal Dynamcis and Embodied Cognition, Gregor Schoner. 31. The Task of Cognitive Neuroscience; Christoph von der Malsburg. 32. Functional Roles of Feedback Signals from Higher-Order Areas to Lower-Order Areas in the Visual Cortical Pathways, Tiande Shou. 33. Dynamics of Population Decoding with Strong Inhibition, Thomas Trappenberg. 34. Global Oscillations of CA3 Neural Fields; Francesco Ventriglia. 35. Behaviour Signatures of Continuous Attractors, Si Wu. 36. Statistical Outliers in Neuron Population and Myelinated Fiber Development in Human Neocortex, David L. Cooper et. al.; 37. Studies on Synchronization Using KIV Model, Mark H. Myers et. al.; 38. Synchronous Firing and Its Control in Neuronal Population with Time Delay, Xianfa Jiao et. al.; 39. Sequence Memory with Dynamic Synapses and Chaotic Neurons, Min Xia et. al.; 40. Interacting Turing and Hopf Instabilities Drive Pattern Formation in a Noise-Driven Model Cortex, Moira L. Steyn-Ross et. al.; 41. Context-Dependent Processing of Spatiotemporal Patterns Based on Interaction Between Neurodynamical Systems, Takashi Hasuo et. al.; 42. The Synchronization and Associative Memory of Izhikevich Neural Network, Wei Zhang et. al.; 43. Connectivity Dependent Effects in Cognitive Neurodynamics of Mental Disorders, Hans Liljenstrom et. al.; 44. Corticopetal Acetylcholine: A Role in Attentional State Transitions and the Genesis of Quasi-Attractors During Perception, Hiroshi Fujii et. al.; 45. Cortical Anatomy and the Spatiotemporal Learning Rule, J.J. Wright et. al.; 46. Theta Phase Precession for Spatial Representation in the Entorhinal-dentate Gyrus-ca3 Network, Colin Molter et. al.; 47. Optical Imaging of Visual Feature Representation in the Awake, Fixating Monkey, Anna Roe et. al.; 48. Mechanisms of Spatial Integration in Primary Visual Cortex of the Primate, Alexander Thiele et. al.; 49. Coding of Peripheral Olfactory Information in the Olfactory Bulb of Small Animals, Fuqiang Xu et. al.; 50. Neural Ensembles and Local Field Potentials in the Hippocampal-Prefrontal Cortex System During Spatial Learning and Strategy Shifts in Rats, Francesco P. Battaglia et. al.; 51. Temporal and Spatial Characters of Retinal Ganglion Cells'Response to Natural Stimuli, Ying-Ying Zhang et. al.; 52. Synchronization of Chaotic Neuronal Networks with Small-world. Topology, Fang Han et. al.; 53. Effects of Time Delay on Synchronization and Temporal Order in a Square Lattice Noisy Neuronal Network, Qing-Yun Wang et. al.; 54. The Attractor Type of Complex Networks, Tan Ning et. al.; 55. Asymptotical Stability of Delayed BAM Neural Networks with Generalized Activation Functions by Comparison Approaches; Yuguo Chen et. al.; 56. Chaotic Synchronization of Hindmarsh-Rose Neurons Coupled by Cubic Nonlinear Feedback, Xiaoling Fang et. al.; Part III Macroscopic Cognitive Neurodynamics. 57. When IsItWorthWorking: Calculating the Motivational Value of Working, Barry J. Richmond et. al.; 58. The Physiological and Biochemical Bases of Functional Brain Imaging, Louis Sokoloff. 59. Through Attention to Consciousness; J.G. Taylor. 60. What Do Infants Infer? Modelling Simple Cognition; J.G. Taylor et. al.; 61. The Time Dimension for Scene Analysis; DeLiang Wang.…