会议名称(中文)： 第五届国际认知神经动力学会议
会议名称(英文)： International Conference on Cognitive Neuroscience
所属学科： 一般力学与力学基础,生物物理学、生物化学及分子生物学,神经科学与心理学
开始日期： 2015-06-03
结束日期： 2015-06-07
所在国家： 中华人民共和国
所在城市： 海南省 三亚市
主办单位： 中国力学学会、华东理工大学
[ 重要日期 ]
全文截稿日期： 2015-02-12
[ 会务组联系方式 ]
联系人： Miss Chris Wang
会议背景介绍：
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.
征文范围及要求：
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 |
