Conferences related to Neurons

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2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

The conference program will consist of plenary lectures, symposia, workshops and invited sessions of the latest significant findings and developments in all the major fields of biomedical engineering. Submitted papers will be peer reviewed. Accepted high quality papers will be presented in oral and poster sessions, will appear in the Conference Proceedings and will be indexed in PubMed/MEDLINE.

  • 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

    The conference will cover diverse topics ranging from biomedical engineering to healthcare technologies to medical and clinical applications. The conference program will consist of invited plenary lectures, symposia, workshops, invited sessions and oral and poster sessions of unsolicited contributions. All papers will be peer reviewed and accepted papers of up to 4 pages will appear in the Conference Proceedings and be indexed by IEEE Xplore and Medline/PubMed.

  • 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

    The conference program will consist of plenary lectures, symposia, workshops and invited sessions of the latest significant findings and developments in all the major fields of biomedical engineering. Submitted papers will be peer reviewed. Accepted high quality papers will be presented in oral and poster sessions, will appear in the Conference Proceedings and will be indexed in PubMed/MEDLINE.

  • 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

    The Annual International Conference of the IEEE Engineering in Medicine and Biology Society covers a broad spectrum of topics from biomedical engineering and physics to medical and clinical applications. The conference program will consist of invited plenary lectures, symposia, workshops, invited sessions, oral and poster sessions of unsolicited contributions. All papers will be peer reviewed and accepted papers of up to 4 pages will appear in the Conference Proceedings and be indexed by PubMed and EI. Prop

  • 2012 34th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

    The annual conference of EMBS averages 2000 attendees from over 50 countries. The scope of the conference is general in nature to focus on the interdisciplinary fields of biomedical engineering. Themes included but not limited to are: Imaging, Biosignals, Biorobotics, Bioinstrumentation, Neural, Rehabilitation, Bioinformatics, Healthcare IT, Medical Devices, etc

  • 2011 33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

    The annual conference of EMBS averages 2000 attendees from over 50 countries. The scope of the conference is general in nature to focus on the interdisciplinary fields of biomedical engineering. Themes included but not limited to are: Imaging, Biosignals, Biorobotics, Bioinstrumentation, Neural, Rehabilitation, Bioinformatics, Healthcare IT, Medical Devices, etc.

  • 2010 32nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

    The annual conference of EMBS averages 2000 attendees from over 50 countries. The scope of the conference is general in nature to focus on the interdisciplinary fields of biomedical engineering. Themes included but not limited to are: Imaging, Biosignals, Biorobotics, Bioinstrumentation, Neural, Rehabilitation, Bioinformatics, Healthcare IT, Medical Devices, etc

  • 2009 31st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

    The annual conference of EMBS averages 2000 attendees from over 50 countries. The scope of the conference is general in nature to focus on the interdisciplinary fields of biomedical engineering. Themes included but not limited to are: Imaging, Biosignals, Biorobotics, Bioinstrumentation, Neural, Rehabilitation, Bioinformatics, Healthcare IT, Medical Devices, etc


2014 IEEE International Symposium on Circuits and Systems (ISCAS)

The IEEE International Symposium on Circuits and Systems (ISCAS) is the flagship conference of the IEEE Circuits and Systems Society and the world

  • 2013 IEEE International Symposium on Circuits and Systems (ISCAS)

    The Symposium will focus on circuits and systems employing nanodevices (both extremely scaled CMOS and non-CMOS devices) and circuit fabrics (mixture of standard CMOS and evolving nano-structure elements) and their implementation cost, switching speed, energy efficiency, and reliability. The ISCAS 2010 will include oral and poster sessions; tutorials given by experts in state-of-the-art topics; and special sessions, with the aim of complementing the regular program with topics of particular interest to the community that cut across and beyond disciplines traditionally represented at ISCAS.

  • 2012 IEEE International Symposium on Circuits and Systems - ISCAS 2012

    2012 International Symposium on Circuits and Systems (ISCAS 2012) aims at providing the world's premier forum of leading researchers in circuits and systems areas from academia and industries, especially focusing on Convergence of BINET (BioInfoNanoEnviro Tech.) which represents IT, NT and ET and leading Human Life Revolutions. Prospective authors are invited to submit papers of their original works emphasizing contributions beyond the present state of the art. We also welcome proposals on special tuto

  • 2011 IEEE International Symposium on Circuits and Systems (ISCAS)

    The IEEE International Symposium on Circuits and Systems (ISCAS) is the world's premier networking forum of leading researchers in the highly active fields of theory, design and implementation of circuits and systems.

  • 2010 IEEE International Symposium on Circuits and Systems - ISCAS 2010

    ISCAS is a unique conference dealing with circuits and systems. It's the yearly "rendez-vous" of leading researchers, coming both from academia and industry, in the highly active fields of theory, design and implementation of circuits and systems. The Symposium will focus on circuits and systems for high quality life and consumer technologies, including mobile communications, advanced multimedia systems, sensor networks and Nano-Bio Circuit Fabrics and Systems.

  • 2009 IEEE International Symposium on Circuits and Systems - ISCAS 2009

    Analog Signal Processing, Biomedical Circuits and Systems, Blind Signal Processing, Cellular Neural Networks and Array Computing, Circuits and Systems for Communications, Computer-Aided Network Design, Digital Signal Processing, Life-Science Systems and Applications, Multimedia Systems and Applications, Nanoelectronics and Gigascale Systems, Neural Systems and Applications, Nonlinear Circuits and Applications, Power Systems and Power Electronic Circuits, Sensory Systems, Visual Signal Processing and Communi

  • 2008 IEEE International Symposium on Circuits and Systems - ISCAS 2008

  • 2007 IEEE International Symposium on Circuits and Systems - ISCAS 2007

  • 2006 IEEE International Symposium on Circuits and Systems - ISCAS 2006


2013 IEEE International Conference on Development and Learning and Epigenetic Robotics (ICDL)

We will discuss how intelligent biological and artificial systems develop sensorimotor, cognitive, and social abilities, over extended periods of time, through dynamic interactions with their physical and social environments. This field lies at the intersection of a number of scientific and engineering disciplines including Neuroscience, Developmental Psychology, Developmental Linguistics, Cognitive Science, Computational Neuroscience, Artificial Intelligence, Machine Learning, and Robotics.

  • 2012 IEEE International Conference on Development and Learning and Epigenetic Robotics (ICDL)

    The ICDL and the Epigenetic Robotics conferences are the premier venues for interdisciplinary research that blends the boundaries between robotics, artificial intelligence, machine learning, developmental psychology, neuroscience, and philosophy. The scope of development and learning covered by this conference includes perceptual, cognitive, motor, behavioral, emotional and other related capabilities that are exhibited by humans, higher animals, artificial systems and robots.

  • 2011 IEEE International Conference on Development and Learning (ICDL)

    This conference covers how intelligent biological and artificial systems develop sensorimotor, cognitive and social abilities, over extended periods of time, through dynamic interactions of their brain and body with their physical and social environments.

  • 2010 IEEE 9th International Conference on Development and Learning (ICDL 2010)

    ICDL is for interdisciplinary research blending between robotics, artificial intelligence, machine learning, developmental psychology, neuroscience, and philosophy. The scope of development and learning covered by this conference includes perceptual, cognitive, motor, behavioral, emotional and other capabilities that are exhibited by humans, higher animals, artificial systems and robots.

  • 2009 IEEE 8th International Conference on Development and Learning (ICDL 2009)

    A multidisciplinary conference pertaining to all subjects related to the development and learning process of natural and artificial systems, including perceptual, cognitive, behavioral, emotional and all other mental capabilities that are exhibited by humans, higher animals, robots.

  • 2008 IEEE 7th International Conference on Development and Learning (ICDL 2008)

    ICDL is a unique interdisciplinary conference that brings together researchers in computational sciences with researchers in biological sciences to focus on issues of development and learning. Submissions often feature collaborative work between computer science, robotics, neuroscience, developmental psychology, and related fields.

  • 2007 IEEE 6th International Conference on Development and Learning (ICDL 2007)

    Computational models of human development and learning; bioinspired mechanisms for robot learning and development

  • 2006 5th International Conference on Development and Learning (ICDL 2006)

  • 2005 4th IEEE International Conference on Development and Learning (ICDL 2005)


2011 23rd Chinese Control and Decision Conference (CCDC)

Chinese Control and Decision Conference is an annual international conference to create a forum for scientists, engineers and practitioners throughout the world to present the latest advancement in Control, Decision, Automation, Robotics and Emerging Technologies.


2010 5th International Summer School on Emerging Technologies in Biomedicine

The aim is to inform young students and researchers about the latest in High Throughput Communication between Brain and Machines . The lectures will be focused on Quantitative Neuroscience, Modern Methods for BCIs, Dynamic Brain Connectivity Mapping, Neuron Based Motor Control, Neuroprosthetics and the tutorial about online BCI system with demonstration.


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Periodicals related to Neurons

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Fuzzy Systems, IEEE Transactions on

Theory and application of fuzzy systems with emphasis on engineering systems and scientific applications. (6) (IEEE Guide for Authors) Representative applications areas include:fuzzy estimation, prediction and control; approximate reasoning; intelligent systems design; machine learning; image processing and machine vision;pattern recognition, fuzzy neurocomputing; electronic and photonic implementation; medical computing applications; robotics and motion control; constraint propagation and optimization; civil, chemical and ...


Neural Networks, IEEE Transactions on

Devoted to the science and technology of neural networks, which disclose significant technical knowledge, exploratory developments, and applications of neural networks from biology to software to hardware. Emphasis is on artificial neural networks.




Xplore Articles related to Neurons

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Recurrent snap-drift neural network for phrase recognition

Dominic Palmer-Brown; Chrisina Draganova The 2010 International Joint Conference on Neural Networks (IJCNN), 2010

A new recurrent neural network is presented, based on the snap-drift algorithm. The simple recurrent network (SRN) architecture is adopted, with the hidden layer values copied back to the input layer. A form of reinforcement learning is deployed in which the mode is swapped between the snap and drift unsupervised modes when performance drops, and in which adaptation is probabilistic, ...


Recognition of heat-conductive filling agents of a thermoelectric refrigeration system with Focused Time — Delay Neural Network

Ivaylo Belovski; Sotir Sotirov; Anatoliy Aleksandrov; Nikolay Sotirov 2017 15th International Conference on Electrical Machines, Drives and Power Systems (ELMA), 2017

Neural Networks are an information processing paradigm that is inspired from the biological nervous systems, such as the human brain, which process information. In the system we use Focused Time-Delay Neural Network for recognizing heat-conductive filling agents It is composed of a large number of highly interconnected processing elements (neurons) working in symbiosis to solve many kinds of problems. In ...


A Logic Connectionist Approach To Self-organized Associative Memory

Wing-Kay Kan Proceedings of the 1988 IEEE International Conference on Systems, Man, and Cybernetics, 1988

First Page of the Article ![](/xploreAssets/images/absImages/00754393.png)


Concurrent Self-Organizing Maps for Supervised/Unsupervised Change Detection in Remote Sensing Images

Victor-Emil Neagoe; Radu-Mihai Stoica; Alexandru-Ioan Ciurea; Lorenzo Bruzzone; Francesca Bovolo IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2014

This paper proposes two approaches to change detection in bitemporal remote sensing images based on concurrent self-organizing maps (CSOM) neural classifier. The first one performs change detection in a supervised way, whereas the second performs change detection in an unsupervised way. The supervised approach is based on two steps: 1) concatenation (CON); and 2) CSOM classification. CSOM classifier uses two ...


Hybrid AI system for geometric pattern recognition

C. G. Fernando; R. Munasinghe Thirty-Sixth Southeastern Symposium on System Theory, 2004. Proceedings of the, 2004

The research area of hybrid and neural processing has been actively developing. Hybrid neural systems are computational systems which are based mainly on artificial neural networks but also allow a symbolic interpretation or interaction with symbolic classical artificial intelligence. In this paper we describe a hybrid AI system developed for 2D object recognition. The 2D object recognition system was developed ...


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Educational Resources on Neurons

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eLearning

Recurrent snap-drift neural network for phrase recognition

Dominic Palmer-Brown; Chrisina Draganova The 2010 International Joint Conference on Neural Networks (IJCNN), 2010

A new recurrent neural network is presented, based on the snap-drift algorithm. The simple recurrent network (SRN) architecture is adopted, with the hidden layer values copied back to the input layer. A form of reinforcement learning is deployed in which the mode is swapped between the snap and drift unsupervised modes when performance drops, and in which adaptation is probabilistic, ...


Recognition of heat-conductive filling agents of a thermoelectric refrigeration system with Focused Time — Delay Neural Network

Ivaylo Belovski; Sotir Sotirov; Anatoliy Aleksandrov; Nikolay Sotirov 2017 15th International Conference on Electrical Machines, Drives and Power Systems (ELMA), 2017

Neural Networks are an information processing paradigm that is inspired from the biological nervous systems, such as the human brain, which process information. In the system we use Focused Time-Delay Neural Network for recognizing heat-conductive filling agents It is composed of a large number of highly interconnected processing elements (neurons) working in symbiosis to solve many kinds of problems. In ...


A Logic Connectionist Approach To Self-organized Associative Memory

Wing-Kay Kan Proceedings of the 1988 IEEE International Conference on Systems, Man, and Cybernetics, 1988

First Page of the Article ![](/xploreAssets/images/absImages/00754393.png)


Concurrent Self-Organizing Maps for Supervised/Unsupervised Change Detection in Remote Sensing Images

Victor-Emil Neagoe; Radu-Mihai Stoica; Alexandru-Ioan Ciurea; Lorenzo Bruzzone; Francesca Bovolo IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2014

This paper proposes two approaches to change detection in bitemporal remote sensing images based on concurrent self-organizing maps (CSOM) neural classifier. The first one performs change detection in a supervised way, whereas the second performs change detection in an unsupervised way. The supervised approach is based on two steps: 1) concatenation (CON); and 2) CSOM classification. CSOM classifier uses two ...


Hybrid AI system for geometric pattern recognition

C. G. Fernando; R. Munasinghe Thirty-Sixth Southeastern Symposium on System Theory, 2004. Proceedings of the, 2004

The research area of hybrid and neural processing has been actively developing. Hybrid neural systems are computational systems which are based mainly on artificial neural networks but also allow a symbolic interpretation or interaction with symbolic classical artificial intelligence. In this paper we describe a hybrid AI system developed for 2D object recognition. The 2D object recognition system was developed ...


More eLearning Resources

IEEE-USA E-Books

  • Neurons, Circuits, and Subsystems

    None

  • Neurons and Circuits

    This chapter contains sections titled: 3.1 Signaling Strategies, 3.2 Receptive Fields, 3.3 Modeling Receptive Field Formation, 3.4 Spike Codes for Cortical Neurons, 3.5 Reflexive Behaviors, 3.6 Summary, 3.7 Appendix: Neuron Basics, Appendix Summary

  • Bibliography

    Neural networks usually work adequately on small problems but can run into trouble when they are scaled up to problems involving large amounts of input data. Circuit Complexity and Neural Networks addresses the important question of how well neural networks scale - that is, how fast the computation time and number of neurons grow as the problem size increases. It surveys recent research in circuit complexity (a robust branch of theoretical computer science) and applies this work to a theoretical understanding of the problem of scalability.Most research in neural networks focuses on learning, yet it is important to understand the physical limitations of the network before the resources needed to solve a certain problem can be calculated. One of the aims of this book is to compare the complexity of neural networks and the complexity of conventional computers, looking at the computational ability and resources (neurons and time) that are a necessary part of the foundations of neural network learning.Circuit Complexity and Neural Networks contains a significant amount of background material on conventional complexity theory that will enable neural network scientists to learn about how complexity theory applies to their discipline, and allow complexity theorists to see how their discipline applies to neural networks.

  • Evolutionary Neural Topiary: Growing and Sculpting Artificial Neurons to Order

    Designing artificial systems with ever more biologically-plausible 'brains' continues apace and permits investigations into the computational capabilities of engineered systems. Creating artificial neurons with biologically-realistic morphologies is however a non-trivial problem. This paper addresses growing neurons to order, neurons with morphologies exhibiting strong biological traits. A biologically-inspired simulator of neural development is coupled with a genetic algorithm to evolve 3-dimensional neuron morphologies. The morphology of a biological neuron provides the exemplar target against which the developmental evolution process is gauged

  • Toward a Circuit Model of Working Memory and the Guidance of Voluntary Motor Action

    This chapter contains sections titled: Physiological Features of Prefrontal Neurons Revealed by Delayed-Response Tasks, Inhibition in Memory Circuits, Sensory Coding in Prefrontal Cortex: Role of Local and Long-Tract Corticocortical Connections, Directional Signaling.: Role of the Corticostriatonigral/Pallidal Projections, Pre- and Postsaccadic Neuronal Activity: Possible Role of Thalamic Innervation, Summary: A Circuit Model for Working Memory, References

  • Instantaneous Cross-Correlation Analysis of Neural Ensembles with High Temporal Resolution

    One of the fundamental difficulties in neural assembly studies is the lack of an effective, high-resolution measure of the spatiotemporal structure in spike trains obtained from a single realization. This chapter proposes a systematic approach to estimate the cross-correlation (CC) of spike trains, over time and in only one realization. The solution lies in an alternate defi nition of cross-correlation which suggests that, rather than time averaging as is current practice, ensemble averaging should be used. This observation suggests a natural instantaneous CC (ICC) estimator as required for high temporal resolution and real-time ensemble analysis and decoding. Results are shown in simulated data sets and neural activity of rat motor cortical neurons during a behavioral task.

  • Temporal Pattern Learning in a Spiking Neuron Chain

    A simple neural network demonstrates lhe ability to learn Morse axle-like temporal patterns, presented via a microphone. A neuronal model, which provides facility for 'spiking' neurons, is used as the basis for this network. The network consists mainly of a chain of neurons, connected so as to lire in sequence. After a period of unsupervised learning, the chain is capable of repeating a temporal pattern upon which it has been trained. The system is particularly robust with respect to noise in the input pattern. Suggestions are made as to how this system could be extended, by replacing the simple chain structure with complex trees and interconnected 'blobs' of neurons. The example of birdsong as a biological system of temporal pattern learning is used as inspiration for further work in this area.

  • Decoding Sensory Stimuli from Populations of Neurons: Methods for LongTerm Longitudinal Studies

    This chapter contains sections titled: Introduction Recording Populations of Single Neurons PSTH-Based Classification of Sensory Stimuli Conclusions and Future Directions Acknowledgments References

  • Hebbian reinforcement learning in a modular dynamic network

    We present a multi-population dynamic neural network model with binary activation and a random interaction pattern. The weights parameters have been specified in order to distinguish excitatory populations from inhibitory populations. Under specific parameters, we design functional modules composed of two populations, one of excitatory neurons, one of inhibitory neurons. Such modules are found to display a weak chaotic activity, and to react toward incoming stimulations with increasing synchronization. We also present the design of topologically structured neural maps. We then combine such modules for the design of a perception/action network composed of one sensory module and two concurrent motor modules. Such a network is coupled with the dynamics of an inverted pendulum, showing spontaneous phase transitions toward various attractors, each attractor corresponding to a particular structural coupling within the agent/environment system. This spontaneous versatility is then exploited in a reinforcement learning paradigm where two separate reinforcement path are defined, one for the positive rewards, the other for the negative rewards. The learning experiment shows a fast adaptation to the constraints, followed by a slower phase where the behavior is improved. No degradation of the behavior is found for continuing learning, i.e. the learned behavior is preserved for long lasting time.

  • Index

    Neural networks usually work adequately on small problems but can run into trouble when they are scaled up to problems involving large amounts of input data. Circuit Complexity and Neural Networks addresses the important question of how well neural networks scale - that is, how fast the computation time and number of neurons grow as the problem size increases. It surveys recent research in circuit complexity (a robust branch of theoretical computer science) and applies this work to a theoretical understanding of the problem of scalability.Most research in neural networks focuses on learning, yet it is important to understand the physical limitations of the network before the resources needed to solve a certain problem can be calculated. One of the aims of this book is to compare the complexity of neural networks and the complexity of conventional computers, looking at the computational ability and resources (neurons and time) that are a necessary part of the foundations of neural network learning.Circuit Complexity and Neural Networks contains a significant amount of background material on conventional complexity theory that will enable neural network scientists to learn about how complexity theory applies to their discipline, and allow complexity theorists to see how their discipline applies to neural networks.



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