IEEE Organizations related to Bio-inspired Computing

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No organizations are currently tagged "Bio-inspired Computing"



Conferences related to Bio-inspired Computing

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2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

FUZZ-IEEE 2021 will represent a unique meeting point for scientists and engineers, both from academia and industry, to interact and discuss the latest enhancements and innovations in the field. The topics of the conference will cover all the aspects of theory and applications of fuzzy sets, fuzzy logic and associated approaches (e.g. aggregation operators such as the Fuzzy Integral), as well as their hybridizations with other artificial and computational intelligence techniques.


2020 IEEE International Symposium on Circuits and Systems (ISCAS)

The International Symposium on Circuits and Systems (ISCAS) is the flagship conference of the IEEE Circuits and Systems (CAS) Society and the world’s premier networking and exchange forum for researchers in the highly active fields of theory, design and implementation of circuits and systems. ISCAS2020 focuses on the deployment of CASS knowledge towards Society Grand Challenges and highlights the strong foundation in methodology and the integration of multidisciplinary approaches which are the distinctive features of CAS contributions. The worldwide CAS community is exploiting such CASS knowledge to change the way in which devices and circuits are understood, optimized, and leveraged in a variety of systems and applications.


IECON 2020 - 46th Annual Conference of the IEEE Industrial Electronics Society

IECON is focusing on industrial and manufacturing theory and applications of electronics, controls, communications, instrumentation and computational intelligence.


2019 IEEE 62nd International Midwest Symposium on Circuits and Systems (MWSCAS)

The IEEE International Midwest Symposium on Circuits and Systems is the oldest IEEE sponsored or co-sponsored conference in the area of analog and digital circuits and systems. Traditional lecture and interactive lecture/poster sessions cover virtually every area of electronic circuits and systems in all fields of interest to IEEE.


2019 19th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID)

Cluster Computing, Grid Computing, Edge Computing, Cloud Computing, Parallel Computing, Distributed Computing



Periodicals related to Bio-inspired Computing

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No periodicals are currently tagged "Bio-inspired Computing"


Most published Xplore authors for Bio-inspired Computing

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Xplore Articles related to Bio-inspired Computing

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Physiological and Bioinspired Systems Development at Obuda University: Research Activities in Budapest, a Reach Across Related Fields for the IEEE Systems, Man, and Cybernetics Society

IEEE Systems, Man, and Cybernetics Magazine, 2019

The main goal of this article is to provide a short summary regarding the research activities of Óbuda University, Budapest, Hungary, related to physiological and bioinspired systems, as one of the key areas of the IEEE Systems, Man, and Cybernetics Society (SMCS). We also intend to introduce the impact of Prof. Imre Rudas through these activities, research institutions, and facilities.


Resistive Memory-Based Analog Synapse: The Pursuit for Linear and Symmetric Weight Update

IEEE Nanotechnology Magazine, 2018

This article reviews the recent developments in a type of random access memory (RAM) called resistive RAM (RRAM) for the analog synapse, which is an important building block for neuromorphic computing systems. To achieve high learning accuracy in an artificial neural network based on the backpropagation learning rule, a linear and symmetric weight update behavior of the analog synapse is ...


From Natural Complexity to Biomimetic Simplification: The Realization of Bionic Fish Inspired by the Cownose Ray

IEEE Robotics & Automation Magazine, 2019

Features of fish-like environmental compatibility and maneuverability have attracted bioinspired design researchers worldwide to contemplate the practical applications of robotic fish. This article presents a conceptual design for and the development of a robotic fish inspired by the cownose ray. We extracted essential biomimetic parameters of the cownose ray to develop reasonable simplifications of the body shape, the mechanical structure ...


Efficient Biosignal Processing Using Hyperdimensional Computing: Network Templates for Combined Learning and Classification of ExG Signals

Proceedings of the IEEE, 2019

Recognizing the very size of the brain's circuits, hyperdimensional (HD) computing can model neural activity patterns with points in a HD space, that is, with HD vectors. Key examined properties of HD computing include: a versatile set of arithmetic operations on HD vectors, generality, scalability, analyzability, one-shot learning, and energy efficiency. These make it a prime candidate for efficient biosignal ...


Continuous-Time Visual-Inertial Odometry for Event Cameras

IEEE Transactions on Robotics, 2018

Event cameras are bioinspired vision sensors that output pixel-level brightness changes instead of standard intensity frames. They offer significant advantages over standard cameras, namely a very high dynamic range, no motion blur, and a latency in the order of microseconds. However, due to the fundamentally different structure of the sensor's output, new algorithms that exploit the high temporal resolution and ...



Educational Resources on Bio-inspired Computing

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IEEE.tv Videos

How Bio-Design Automation Can Help Reboot Computing: Lessons, Challenges, and Future Directions - IEEE Rebooting Computing 2017
Brain Inspired Computing Systems - Luping Shi: 2016 International Conference on Rebooting Computing
EMBC 2011-Speaker Highlights-Mary Tolikas, PhD, MBA
IROS TV 2019- Macau- Episode 1- Robots Connecting People
IROS TV 2019- How to Build a Robot: Marine Bio-inspired Soft Robotics
Robotics History: Narratives and Networks Oral Histories: Radhika Nagpal
Q&A with Dejan Milojicic: IEEE Rebooting Computing Podcast, Episode 26
Q&A with Scott Koziel: IEEE Rebooting Computing Podcast, Episode 23
Workshop on Human-Swarm Interaction-ICRA 2020
Life Sciences: Rashid Bashir and using Bio Nanotech to solve medical problems
Hyperdimensional Biosignal Processing: A Case Study for EMG-based Hand Gesture Recognition - Abbas Rahimi: 2016 International Conference on Rebooting Computing
Broadening Participation in Computing and Empowering Female Leaders with Maria Klawe - IEEE WIE ILC 2017
Technology Discourse: Bio Fuels
IEEE Highlight: Electronic Nose: Diagnosing Cancer Through Smell
IMS 2015: Four scientists who saved Maxwells Theory
Octopus-Inspired Robot Can Grasp, Crawl and Swim -- IEEE Spectrum Report
IMS 2015: Maxwell's Equation - The Genesis
Robotics History: Narratives and Networks Oral Histories: Rolf Pfiefer
Multiobjective Quantum-inspired Evolutionary Algorithm and Preference-based Solution Selection Algorithm
Bringing Biological Models to Life: The Power of Agent-based Modeling and Visualization

IEEE-USA E-Books

  • Physiological and Bioinspired Systems Development at Obuda University: Research Activities in Budapest, a Reach Across Related Fields for the IEEE Systems, Man, and Cybernetics Society

    The main goal of this article is to provide a short summary regarding the research activities of Óbuda University, Budapest, Hungary, related to physiological and bioinspired systems, as one of the key areas of the IEEE Systems, Man, and Cybernetics Society (SMCS). We also intend to introduce the impact of Prof. Imre Rudas through these activities, research institutions, and facilities.

  • Resistive Memory-Based Analog Synapse: The Pursuit for Linear and Symmetric Weight Update

    This article reviews the recent developments in a type of random access memory (RAM) called resistive RAM (RRAM) for the analog synapse, which is an important building block for neuromorphic computing systems. To achieve high learning accuracy in an artificial neural network based on the backpropagation learning rule, a linear and symmetric weight update behavior of the analog synapse is critical. The physical mechanisms in the RRA M (interfacing switching versus filamentary switching) are discussed, and the pros and cons of each mechanism to emulate the analog synaptic weights are compared. Then, various strategies from a materials and device engineering perspective are surveyed to achieve linearly and symmetric conductance changes under identical pulses. Finally, future research directions are outlined.

  • From Natural Complexity to Biomimetic Simplification: The Realization of Bionic Fish Inspired by the Cownose Ray

    Features of fish-like environmental compatibility and maneuverability have attracted bioinspired design researchers worldwide to contemplate the practical applications of robotic fish. This article presents a conceptual design for and the development of a robotic fish inspired by the cownose ray. We extracted essential biomimetic parameters of the cownose ray to develop reasonable simplifications of the body shape, the mechanical structure design principle of the multijointdriving fin rays, and the motion principle. The practical motion abilities of the bionic prototype's internally driven skeleton are calculated theoretically and compared with its natural model. Parameters affecting propulsion performances are analyzed using a one- dimensional (1-D) calculation method. The basic motion modes are obtained according to the analysis.

  • Efficient Biosignal Processing Using Hyperdimensional Computing: Network Templates for Combined Learning and Classification of ExG Signals

    Recognizing the very size of the brain's circuits, hyperdimensional (HD) computing can model neural activity patterns with points in a HD space, that is, with HD vectors. Key examined properties of HD computing include: a versatile set of arithmetic operations on HD vectors, generality, scalability, analyzability, one-shot learning, and energy efficiency. These make it a prime candidate for efficient biosignal processing where signals are noisy and nonstationary, training data sets are not huge, individual variability is significant, and energy-efficiency constraints are tight. Purely based on native HD computing operators, we describe a combined method for multiclass learning and classification of various ExG biosignals such as electromyography (EMG), electroencephalography (EEG), and electrocorticography (ECoG). We develop a full set of HD network templates that comprehensively encode body potentials and brain neural activity recorded from different electrodes into a single HD vector without requiring domain expert knowledge or ad hoc electrode selection process. Such encoded HD vector is processed as a single unit for fast one-shot learning, and robust classification. It can be interpreted to identify the most useful features as well. Compared to state-of-the-art counterparts, HD computing enables online, incremental, and fast learning as it demands less than a third as much training data as well as less preprocessing.

  • Continuous-Time Visual-Inertial Odometry for Event Cameras

    Event cameras are bioinspired vision sensors that output pixel-level brightness changes instead of standard intensity frames. They offer significant advantages over standard cameras, namely a very high dynamic range, no motion blur, and a latency in the order of microseconds. However, due to the fundamentally different structure of the sensor's output, new algorithms that exploit the high temporal resolution and the asynchronous nature of the sensor are required. Recent work has shown that a continuous- time representation of the event camera pose can deal with the high temporal resolution and asynchronous nature of this sensor in a principled way. In this paper, we leverage such a continuous-time representation to perform visual- inertial odometry with an event camera. This representation allows direct integration of the asynchronous events with microsecond accuracy and the inertial measurements at high frequency. The event camera trajectory is approximated by a smooth curve in the space of rigid-body motions using cubic splines. This formulation significantly reduces the number of variables in trajectory estimation problems. We evaluate our method on real data from several scenes and compare the results against ground truth from a motion- capture system. We show that our method provides improved accuracy over the result of a state-of-the-art visual odometry method for event cameras. We also show that both the map orientation and scale can be recovered accurately by fusing events and inertial data. To the best of our knowledge, this is the first work on visual-inertial fusion with event cameras using a continuous- time framework.

  • Neuromorphic Computing Using Memristor Crossbar Networks: A Focus on Bio-Inspired Approaches

    Neuromorphic computing systems, which employ electronic circuits based on digital and analog components to mimic the neurobiological structures in nervous systems, have attracted broad interest as a promising approach for future computing applications [1]-[3]. The interest is driven both from the bottom-up, where continued performance gains following Moore's law have become increasingly harder to obtain [4], [5], and from the top-down, where prolific applications of data-centric tasks such as artificial intelligence [6]-[8], bigdata analysis [9]-[11], and large-scale numerical simulations [12], [13] demand computer architectures that can efficiently address the von Neumann bottleneck [14]. Neuromorphic computing allows massive amounts of data to be processed in parallel, potentially with minimal data movement, thus offering a promising solution for current and future computing needs.

  • Construction of a Genetic Comparator in Escherichia Coli

    Construction of biomolecular computing devices has become a hot topic. Although many biomolecular computing devices have been developed, cellular comparators, which play a critical role in facilitating decision-making, are still challenging to create. A comparator requires a computing tool that can determine values based on judging the existence of a signal. It is challenging to find such a computing tool. The clustered regularly interspaced short palindromic repeats/DNase-dead Cpf1 (CRISPR/ddCpf1) system is a novel expressing interference system with high specificity and programmability. In this paper, we have successfully designed, constructed, and tested a genetic comparator in the bacterium Escherichia coli using the CRISPR/ddCpf1 system. The comparator could indicate the maximum value for up to three signals. The comparator may have many potential applications in decision-making tasks, such as biocomputing, biotherapy, bioremediation, and artificial intelligence. In addition, our results provide important insights into the use of the CRISPR/ddCpf1 system to establish complex cellular computing devices.

  • Structure-Guided Protein Transition Modeling with a Probabilistic Roadmap Algorithm

    Proteins are macromolecules in perpetual motion, switching between structural states to modulate their function. A detailed characterization of the precise yet complex relationship between protein structure, dynamics, and function requires elucidating transitions between functionally-relevant states. Doing so challenges both wet and dry laboratories, as protein dynamics involves disparate temporal scales. In this paper, we present a novel, sampling-based algorithm to compute transition paths. The algorithm exploits two main ideas. First, it leverages known structures to initialize its search and define a reduced conformation space for rapid sampling. This is key to address the insufficient sampling issue suffered by sampling-based algorithms. Second, the algorithm embeds samples in a nearest-neighbor graph where transition paths can be efficiently computed via queries. The algorithm adapts the probabilistic roadmap framework that is popular in robot motion planning. In addition to efficiently computing lowest-cost paths between any given structures, the algorithm allows investigating hypotheses regarding the order of experimentally-known structures in a transition event. This novel contribution is likely to open up new venues of research. Detailed analysis is presented on multiple-basin proteins of relevance to human disease. Multiscaling and the AMBER ff14SB force field are used to obtain energetically-credible paths at atomistic detail.

  • Building Brain-Inspired Computing Systems: Examining the Role of Nanoscale Devices

    Brain-inspired computing is attracting considerable attention because of its potential to solve a wide variety of data-intensive problems that are difficult for even state-of-the-art supercomputers to tackle. The ability of the human brain to process visual and audio inputs in real time and make complex logical decisions by consuming a mere 20 W makes it the most power- efficient computational engine known to man. While state-of-the-art digital complimentary metal-oxide-semiconductor (CMOS) technology permits the realization of individual devices and circuits that mimic the dynamics of neurons and synapses in the brain, emulating the immense parallelism and event-driven computational architecture in systems with comparable complexity and power budget as the brain, and in real time, remains a formidable challenge.

  • The Role of Short-Term Plasticity in Neuromorphic Learning: Learning from the Timing of Rate-Varying Events with Fatiguing Spike-Timing-Dependent Plasticity

    Neural networks (NNs) have been able to provide record-breaking performance in several machine-learning tasks, such as image and speech recognition, natural- language processing, playing complex games, and data analytics for scientific or business purposes [1]. They process their inputs through a series of linear and nonlinear operations and use learning algorithms, i.e., rules that optimize the parameters of the network.



Standards related to Bio-inspired Computing

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No standards are currently tagged "Bio-inspired Computing"