Article
Chemistry, Multidisciplinary
Gang Zhou, Tinghui Li, Rong Huang, Peifang Wang, Bin Hu, Hao Li, Lizhe Liu, Yan Sun
Summary: Inspired by signal memory in a spiking neural network, a rechargeable catalyst technology was developed to activate and remember optimal catalytic activity, leading to significantly improved efficiency in the synthesis of NH3 from N-2. The designed FeReS3 Janus layers mimicking a multiple-neuron network enable intriguing multiphase transitions to activate undiscovered catalytic activity, with a remarkable Faradaic efficiency and high rate of NH3 synthesis. This rechargeable catalyst demonstrates unprecedented catalytic performance lasting for up to 216 hours and can be repeatedly activated through a simple charging operation.
JOURNAL OF THE AMERICAN CHEMICAL SOCIETY
(2021)
Article
Computer Science, Artificial Intelligence
Sheril Lawrence, Aishwarya Yandapalli, Shrisha Rao
Summary: With the increasing demand for processing power in computing applications, there is a push to find alternative computing architectures that require less resources. Neuromorphic computing is showing promise as a direction that achieves high speeds with less resources, mainly focusing on solving machine learning and pattern recognition problems. By proposing a solution using neuromorphic architectures to solve matrix multiplication, significant performance improvement has been demonstrated in terms of processing time and intermediate storage over existing approaches.
Article
Multidisciplinary Sciences
Verena Brehm, Johannes W. Austefjord, Serban Lepadatu, Alireza Qaiumzadeh
Summary: The proposed neuromorphic computing model based on antiferromagnetic domain walls can mimic the behavior of biological neurons and has faster processing speed and more functionalities compared to previous models based on ferromagnetic systems.
SCIENTIFIC REPORTS
(2023)
Article
Biochemical Research Methods
Brian Nils Lundstrom, Thomas J. J. Richner
Summary: The relationship between macro-scale electrophysiological recordings and underlying neural activity dynamics is unclear. Low frequency EEG activity is decreased while higher frequency activity is increased at the seizure onset zone (SOZ). These changes result in power spectral density (PSD) with flattened slopes near the SOZ, indicating increased excitability. Multiple timescale adaptation affects the PSDs and can approximate fractional dynamics. Increased input combined with loss of adaptation reduces low frequency activity and increases higher frequency activity, consistent with clinical EEG observations.
PLOS COMPUTATIONAL BIOLOGY
(2023)
Article
Computer Science, Artificial Intelligence
Yun Zhang, Hong Qu, Xiaoling Luo, Yi Chen, Yuchen Wang, Malu Zhang, Zefang Li
Summary: In this paper, a Recursive Least Squares-Based Learning Rule (RLSBLR) for Spiking Neural Networks (SNNs) is proposed to generate desired spatio-temporal spike trains. Experimental results in different settings show that the proposed RLSBLR outperforms competitive algorithms in terms of learning accuracy, efficiency, and robustness against noise. The integration of modified synaptic delay learning further improves the learning performance.
Article
Multidisciplinary Sciences
Cayla M. Miller, Elgin Korkmazhan, Alexander R. Dunn
Summary: This study presents a method to extract actin filament velocities in living cells and compares them to current models of cytoskeletal dynamics. The authors found that the motion of actin filaments is better described by a statistical jump process than continuous, diffusive movement models. This suggests that a common physical model can potentially explain actin filament dynamics in various cellular contexts.
NATURE COMMUNICATIONS
(2022)
Article
Mathematics, Interdisciplinary Applications
J. Yang, E. Primo, D. Aleja, R. Criado, S. Boccaletti, K. Alfaro-Bittner
Summary: Boolean logic is the foundation of modern computation, and we have discovered that it is possible to emulate Boolean logic through adaptive synchronization in nonlinear dynamical systems. We have demonstrated that using Hodgkin-Huxley model spiking neurons as basic computational units, all 16 Boolean logical gates with two inputs and one output can be realized.
CHAOS SOLITONS & FRACTALS
(2022)
Article
Quantum Science & Technology
Craig Gidney, Martin Ekera
Summary: This study significantly reduces the cost of factoring integers and computing discrete logarithms in finite fields on a quantum computer by combining techniques from various sources. Plausible physical assumptions for large-scale superconducting qubit platforms are used to estimate the approximate cost of the construction. Factors normally ignored are taken into account, leading to more accurate results than previous studies.
Article
Computer Science, Artificial Intelligence
Zhongyi Han, Xian-Jin Gui, Haoliang Sun, Yilong Yin, Shuo Li
Summary: In this paper, a noise-robust domain adaptation method is proposed to address the issue of corrupted source domain examples in multiple noisy environments. By utilizing offline curriculum learning, gradually decreasing noisy distribution distance, estimating open-set noise degree, robust parameter learning, and domain-invariant feature learning, these components are seamlessly transformed into an adversarial network for efficient joint optimization, leading to significant improvements in transfer tasks.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
(2023)
Review
Chemistry, Multidisciplinary
Hefei Liu, Yuan Qin, Hung-Yu Chen, Jiangbin Wu, Jiahui Ma, Zhonghao Du, Nan Wang, Jingyi Zou, Sen Lin, Xu Zhang, Yuhao Zhang, Han Wang
Summary: This paper reviews the progress of artificial neuronal devices based on emerging volatile switching materials, focusing on the demonstrated neuron models implemented in these devices and their utilization for computational and sensing applications. Furthermore, it discusses the inspirations from neuroscience and engineering methods to enhance the neuronal dynamics that are yet to be realized in artificial neuronal devices and networks towards achieving the full functionalities of biological neurons.
ADVANCED MATERIALS
(2023)
Article
Engineering, Mechanical
Hesham A. Elkaranshawy, Nermeen M. Aboukelila, Hanaa M. Elabsy
Summary: The research investigates the synchronization and desynchronization of an array of non-identical Izhikevich neurons in a star-like configuration with the use of adaptive stable filters. The study demonstrates the success of the proposed technique in numerical simulations, specifying parameters governing filter performance and identifying limitations of the methodology.
NONLINEAR DYNAMICS
(2021)
Article
Telecommunications
Miguel Lopez-Benitez, Mohammed M. Alammar
Summary: This paper proposes a novel method for automatically extracting the bandwidth and start/end times of each transmission in a spectrogram. Simulation and experimental results demonstrate that the proposed method achieves higher accuracy compared to existing methods.
IEEE TRANSACTIONS ON COGNITIVE COMMUNICATIONS AND NETWORKING
(2022)
Article
Multidisciplinary Sciences
Silvan Huerkey, Nelson Niemeyer, Jan-Hendrik Schleimer, Stefanie Ryglewski, Susanne Schreiber, Carsten Duch
Summary: This study identifies a miniaturized circuit solution for the central-pattern-generating (CPG) neural network underlying insect asynchronous flight. The network consists of motoneurons interconnected by electrical synapses that produce network activity splayed out in time instead of synchronized across neurons. This mechanism translates unpatterned premotor input into stereotyped neuronal firing with fixed sequences of cell activation, ensuring stable wingbeat power and is conserved across multiple species.
Article
Agriculture, Multidisciplinary
Whoi Cho, Abby ShalekBriski, B. Wade Brorsen, Davood Poursina
Summary: Precision agriculture requires combining multiple measurement methods. This study proposes a method that utilizes Bayesian Kriging to estimate the joint spatial distribution of measurements and uses Bayesian Decision Theory and a grid search procedure to determine the economic optimum of these measurements. Comparison with other methods demonstrates the accuracy and economic value of this approach in soil mapping.
PRECISION AGRICULTURE
(2022)
Article
Psychology, Biological
Zoe M. Boundy-Singer, Corey M. Ziemba, Robbe L. T. Goris
Summary: The study reveals that people's confidence estimates reflect decision reliability rather than accuracy, and the quality of confidence judgments is limited by the uncertainty about stimulus uncertainty. People tend to report higher confidence in easy decisions compared to difficult ones, but confidence reports do not perfectly match decision accuracy and are influenced by response biases and difficulty misjudgements. To understand the quality of confidence reports, a model of the decision-making process underlying choice-confidence data was developed, which shows that confidence reflects a person's estimate of decision reliability, limited by their uncertainty about the uncertainty of the information.
NATURE HUMAN BEHAVIOUR
(2023)
Article
Neurosciences
Sander W. Keemink, Mark C. W. van Rossum
Article
Computer Science, Artificial Intelligence
Paolo Puggioni, Marta Jelitai, Ian Duguid, Mark C. W. van Rossum
NEURAL COMPUTATION
(2017)
Review
Biology
Rui Ponte Costa, Beatriz E. P. Mizusaki, P. Jesper Sjostrom, Mark C. W. van Rossum
PHILOSOPHICAL TRANSACTIONS OF THE ROYAL SOCIETY B-BIOLOGICAL SCIENCES
(2017)
Review
Biology
Tara Keck, Taro Toyoizumi, Lu Chen, Brent Doiron, Daniel E. Feldman, Kevin Fox, Wulfram Gerstner, Philip G. Haydon, Mark Huebener, Hey-Kyoung Lee, John E. Lisman, Tobias Rose, Frank Sengpiel, David Stellwagen, Michael P. Stryker, Gina G. Turrigiano, Mark C. van Rossum
PHILOSOPHICAL TRANSACTIONS OF THE ROYAL SOCIETY B-BIOLOGICAL SCIENCES
(2017)
Article
Neurosciences
Sander W. Keemink, Clemens Roucsein, Mark C. W. van Rossum
JOURNAL OF NEUROPHYSIOLOGY
(2018)
Article
Multidisciplinary Sciences
Sander W. Keemink, Scott C. Lowe, Janelle M. P. Pakan, Evelyn Dylda, Mark C. W. van Rossum, Nathalie L. Rochefort
SCIENTIFIC REPORTS
(2018)
Article
Computer Science, Artificial Intelligence
Sander W. Keemink, Dharmesh V. Tailor, Mark C. W. van Rossum
NEURAL COMPUTATION
(2018)
Article
Behavioral Sciences
David Acunzo, Graham MacKenzie, Mark C. W. van Rossum
COGNITIVE AFFECTIVE & BEHAVIORAL NEUROSCIENCE
(2019)
Article
Mathematical & Computational Biology
Maxime Froc, Mark C. W. van Rossum
JOURNAL OF COMPUTATIONAL NEUROSCIENCE
(2019)
Article
Biology
Michael Jan Fauth, Mark C. W. van Rossum
Article
Biology
Ho Ling Li, Mark C. W. van Rossum
Article
Biology
Jiamu Jiang, Paul Smith, Mark C. W. van Rossum
BULLETIN OF MATHEMATICAL BIOLOGY
(2020)
Article
Mathematical & Computational Biology
Albert Albesa-Gonzalez, Maxime Froc, Oliver Williamson, Mark C. W. van Rossum
Summary: Models of synaptic plasticity, such as the BCM model, have been used to study neural development and learning. By incorporating feedforward inhibition and experimental observations, the modified BCM model provides insights into the effects of synaptic strength and competition on learning outcomes.
JOURNAL OF COMPUTATIONAL NEUROSCIENCE
(2022)
Article
Neurosciences
Sofia Isabel Ribeiro Pereira, Lorena Santamaria, Ralph Andrews, Elena Schmidt, Mark C. W. Van Rossum, Penelope Lewis
Summary: This study aimed to investigate whether triggering memory reactivation in sleep can facilitate the process of abstraction. The findings revealed that reactivating memory during REM sleep improved performance on abstraction problems, but not during SWS sleep. Interestingly, this improvement was not significant until a follow-up retest 1 week later, suggesting that REM sleep may initiate a sequence of plasticity events that take time to unfold.
JOURNAL OF NEUROSCIENCE
(2023)
Article
Neurosciences
Aaron Pache, Mark C. W. van Rossum
Summary: This review explores different ways to reduce energy requirements for learning in neural networks, and discusses how energy efficiency may have influenced biological learning by comparing learning rules with cognitive and neurophysiological observations.
CURRENT OPINION IN NEUROBIOLOGY
(2023)
