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Studying cognition from a dynamic view has become a trend currently, and rapid developments have taken place in nonlinear dynamics and cognitive science. In order to promote the integration of cognitive science and neurodynamics as a whole, the 5th International Conference on Cognitive Neurodynamics 2015 (ICCN 2015) will be hosted by East China University of Science and Technology (ECUST) in Sanya, China during June 3-7, 2015. The conference will provide a forum for scientists and engineers working in the area and its related fields to review the latest progress and development and to exchange their experience, research progress and ideas. An Editorial Board Meeting of Cognitive Neurodynamics will also be held during the period. The conference will consist of three-day oral and poster presentations, and a one-day tour in Sanya city, which is one of the most beautiful garden cities in China.

征稿信息

重要日期

2015-02-12
初稿截稿日期

征稿范围

1. Microscopic CN 1.1 Molecular 1.1.1 DNA, genetic knock-out models 1.1.2 Nonsynaptic diffusion neurotransmission 1.1.3 Synaptic molecular mechanisms 1.1.4 Quantum mechanics, microtubules, synaptic vesicles 1.2 Synaptic 1.2.1 Synaptic dynamics, short term: facilitation, depression 1.2.2 Synaptic dynamics, long term: LTP LTD 1.3 Cellular 1.3.1 Feature detector cells, unit correlates 1.3.2 Cardinal cells, synfire chains, binding 1.3.3 Dynamic neural coding, rate, interval 1.4 Realistic Neural Network dynamics 1.4.1 Synchronization, entrainment 1.4.2 Spiking neural nets 1.4.3 Oscillations 2. Mesoscopic CN; Transitions between Levels 2.1 Population dynamics 2.1.1 Linear systems analysis 2.1.2 Dynamical and use-dependent nonlinearities 2.1.3 Static and amplitude-dependent nonlinearities 2.2 Chaotic dynamics 2.2.1 Deterministic, low-dimensional 2.2.2 Stochastic, infinite-dimensional 2.2.3 Nonconvergent attractor landscapes/networks 2.2.4 Chaotic Trajectories, Itinerancy 2.3 Phase transitions in excitable media 2.3.1 Conditional stability of neural systems 2.3.2 Metastability of coordinated dynamics 2.3.3 Stochastic resonance in sensory information processing 2.4 Complexity theory applied to brain 2.5 Synergetics, metastability 2.6 Quantum Field Theory 2.6.1 Correlation length 2.6.2 Spontaneous symmetry breaking, phase transition 2.6.3 Dissipative systems, multiple ground states 2.7 Neuropercolation 2.8 Self-assembly, artificial life 3. Macroscopic CN 3.1 Brain imaging 3.1.1 fMRI, PET, SPECT, BOLD, Diffusion Tensor 3.1.2 Magnetoencephalography MEG 3.1.3 Electroencephalography EEG 3.1.4 Event-Related Potentials ERP 3.1.5 Topographic Mapping 3.1.6 Divergent/convergent spatiotemporal integral transforms 3.1.7 Multidimensional scaling and pattern classification 3.1.8 Brain connectivity, quantitative neuroanatomy 3.1.9 Relation between neural activity and brain imaging 3.2 Sensory Dynamics 3.2.1 Computational vision 3.2.2 Computational audition 3.2.3 Olfaction and taste coding 3.2.4 Somato-sensation 3.2.5 Vestibular function and proprioception 3.3 Motor system dynamics 3.3.1 Motor and premotor cortical dynamics 3.3.2 Basal ganglia, thalamus 3.3.3 Brain stem neuromodulators 3.3.4 Cerebellar sensorimotor control 3.3.5 Locomotion, vestibular regulation 3.3.6 Oculomotor function 3.3.7 Coordination Dynamics 3.3.8 Perception-Action 3.3.9 Internal models (both forward and inverse with learning) 3.4 Navigation 3.4.1 Hippocampal cognitive mapping 3.4.2 Place cells, sequential learning 3.4.3 Orientation by landmarks vs. contiguous sites 3.5 Action planning and control 3.5.1 Attention, expectancy 3.5.2 Reafference and preafference 3.5.3 Hierarchical goal construction 3.5.4 Neural prediction and hypothesis testing 3.6 Learning and memory 3.6.1 General learning rules 3.6.2 Spike-timing dependent (STDP) learning rules 3.6.3 Reinforcement learning rules (TD and ACE-style) 3.6.4 Back-propagation-style learning rules (BEP) 3.6.5 Memory storage 3.6.6 Memory retrieval 3.6.7 Working memory 3.7 Global cognitive functions 3.7.1 Object recognition 3.7.2 Attention 3.7.3 Intention 3.7.4 Language, neurolinguistics, semiotics 3.7.5 Decision Making 3.7.6 Reasoning and planning 3.7.7 Emotion 3.7.8 Consciousness 3.7.9 Gestalt Phenomena 3.7.10 Altered states, hallucination 4. Applications 4.1 Neural Engineering 4.1.1 Reverse engineering of biological systems 4.1.2 Self-organizing neurodynamics 4.1.3 Self-organized criticality (SOC), highly optimized tolerance (HOT) 4.1.4 Scale-free neocortical dynamics 4.1.5 Brain-Machine Interfaces 4.1.6 Sensory substitution 4.2 Neurocomputer 4.3 Neural computing 4.3.1 Feedforward: MLP, RBF, supervised learning 4.3.2 Feedback: autoassociator, SOFM, backpropagation, ART 4.3.3 Boltzmann machines, simulated annealing 4.3.4 Statistical neural fields 4.3.5 Cellular neural networks, cellular automata, deterministic & random 4.3.6 Chaotic neural networks 4.4 Advanced robotics 4.4.1 Interactive group robotics 4.4.2 Intelligent robotics 4.4.3 Intentional robotics 4.4.4 Autonomic regulation and systems control 4.5 Behavior modification 4.5.1 Bonding, affiliation 4.5.2 Transference 4.5.3 Brainwashing 4.5.4 Conversion, political, religious 4.5.5 Epiphany, insight, creative transformation 4.5.6 Dynamic diseases in nervous systems
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重要日期
  • 会议日期

    06月03日

    2015

    06月07日

    2015

  • 02月12日 2015

    初稿截稿日期

  • 06月07日 2015

    注册截止日期

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