23 papers · ranked by Valyu relevance
Yuanrui Dong, Shirong Wang, Qiang Huang, Rune W. Berg + 2 more
Brain-computer interfaces have revolutionized the field of neuroscience by providing a solution for paralyzed patients to control external devices and improve the quality of daily life. To accurately and stably control effectors, it is important for decoders to recognize an individual's motor intention from neural…
Benjamin I. Rapoport, Lorenzo Turicchia, Woradorn Wattanapanitch, Thomas J. Davidson + 2 more
'Thomas J. Davidson' 'Rahul Sarpeshkar' 'Michal Zochowski'] The ability to decode neural activity into meaningful control signals for prosthetic devices is critical to the development of clinically useful brain- machine interfaces (BMIs). Such systems require input from tens to hundreds of brain-implanted recording…
Yu Song, Liyuan Han, Bo Xu, Tielin Zhang
neural signal decoding Authors: ['Yu Song' 'Liyuan Han' 'Bo Xu' 'Tielin Zhang'] Brain-computer interfaces (BCIs) are an advanced fusion of neuroscience and artificial intelligence, requiring stable and long-term decoding of neural signals. Spiking Neural Networks (SNNs), with their neuronal dynamics and spike-based…
Cameron Higgins, Mats W.J. van Es, Andrew J. Quinn, Diego Vidaurre + 1 more
'Mark W. Woolrich'] Title: Highlights 1. • We investigate different decoding paradigms applied to epoched data and characterise the information content available to each over time. 2. • Under commonly used instantaneous signal decoding paradigms, sinusoidal components of the evoked response are translated to double…
Jacek P. Dmochowski, Jason Ki, Paul DeGuzman, Paul Sajda + 1 more
In neuroscience, stimulus-response relationships have traditionally been analyzed using either encoding or decoding models. Here we combined both techniques by decomposing neural activity into multiple components, each representing a portion of the stimulus. We tested this hybrid approach on encephalographic responses…
Caroline Haimerl, Douglas A. Ruff, Marlene R. Cohen, Cristina Savin + 1 more
Sensory-guided behavior requires reliable encoding of stimulus information in neural responses, and task-specific decoding through selective combination of these responses. The former has been the topic of intensive study, but the latter remains largely a mystery. We propose a framework in which shared stochastic…
Seungbin Park, Megan Lipton, Maria C. Dadarlat
Two-photon imaging has been a critical tool for dissecting brain circuits and understanding brain function. However, relating slow two-photon calcium imaging data to fast behaviors has been challenging due to relatively low imaging sampling rates, thus limiting potential applications to neural prostheses. Here, we show…
Douglas L. Jones, Erik C. Johnson, Rama Ratnam
A neural code based on sequences of spikes can consume a significant portion of the brain's energy budget. Thus, energy considerations would dictate that spiking activity be kept as low as possible. However, a high spike-rate improves the coding and representation of signals in spike trains, particularly in sensory…
Mingkang Li, Ruixue Wang, Guihua Wan, Yuqi Yang + 1 more
Calcium imaging has gained extensive application in neural decoding tasks because of its high precision in observing cortical neural activity. Nevertheless, the immense data volume and complexity of automated signal extraction algorithms in calcium imaging result in significant delays in extracting neuronal calcium…
Nur Ahmadi, Timothy G. Constandinou, Christos-Savvas Bouganis
Robustness and decoding accuracy remain major challenges in the clinical translation of intracortical brain-machine interface (BMI) systems. In this work, we show that a signal/decoder co-design methodology (exploiting the synergism between the input signal and decoding algorithm within the design development process)…
Caroline Haimerl, Cristina Savin, Eero P. Simoncelli
Sensory-guided behavior requires reliable encoding of information (from stimuli to neural responses) and flexible decoding (from neural responses to behavior). In typical decision tasks, a small subset of cells within a large population encode task-relevant stimulus information and need to be identified by later…
Zijun Wan, Tengjun Liu, Xingchen Ran, Pengfu Liu + 2 more
'Shaomin Zhang'] Introduction Intracortical Brain-Computer Interfaces (iBCI) establish a new pathway to restore motor functions in individuals with paralysis by interfacing directly with the brain to translate movement intention into action. However, the development of iBCI applications is hindered by the…
Nikolaus Kriegeskorte, Pamela K. Douglas
Encoding and decoding models are widely used in systems, cognitive, and computational neuroscience to make sense of brain-activity data. However, the interpretation of their results requires care. Decoding models can help reveal whether particular information is present in a brain region in a format the decoder can…
Theodore W. Berger, Zhe (Sage) Chen, Andrzej Cichocki, Karim G. Oweiss + 2 more
'Karim G. Oweiss' 'Rodrigo Quian Quiroga' 'Nitish V. Thakor'] Signal processing and statistics have been playing a pivotal role in computational neuroscience and neural engineering research. Advances in technology have enabled us to simultaneously record extracellular neuronal signals through hundreds of electrode…
Hamid Karimi-Rouzbahani, Mozhgan Shahmohammadi, Ehsan Vahab, Saeed Setayeshi + 1 more
How does the human brain encode visual object categories? Our understanding of this has advanced substantially with the development of multivariate decoding analyses. However, conventional electroencephalography (EEG) decoding predominantly use the “mean” neural activation within the analysis window to extract category…
Filippo Costa, Chiara De Luca
The encoder has low model complexity, relying on a shallow network of heterogeneous neurons. It relies on an internal time reference, allowing for continuous processing. Moreover, stimulus parameters can be linearly decoded from the spiking patterns, granting fast information retrieval. Our approach, validated on both…
Simon R. Schultz, Robin A. A. Ince, Stefano Panzeri
Information theory is a practical and theoretical framework developed for the study of communication over noisy channels. Its probabilistic basis and capacity to relate statistical structure to function make it ideally suited for studying information flow in the nervous system. It has a number of useful properties: it…
Yang Tian, Guoqi Li, Pei Sun
The brain works as a dynamic system to process information. Various challenges remain in understanding the connection between information and dynamics attributes in the brain. The present research pursues exploring how the characteristics of neural information functions are linked to neural dynamics. We attempt to…
Zihan Pan, Jibin Wu, Malu Zhang, Haizhou Li + 1 more
—Neural encoding plays an important role in faithfully describing the temporally rich patterns, whose instances include human speech and environmental sounds. For tasks that involve classifying such spatio-temporal patterns with the Spiking Neural Networks (SNNs), how these patterns are encoded directly influence the…
Pietro Savazzi, Anna Vizziello, Fabio Dell’Acqua
—Wireless Spiking neural networks (WSNNs) allow energy-efficient device-to-device (D2D) or vehicle-to-everything (V2X) communications, especially while considering edge intelligence and learning for beyond 5G and 6G systems. Recent research work has revealed that distributed wireless SNNs (DWSNNs) show good performance…
Authors not listed
Electrospray ionization (ESI) mass spectrometry is an essential technique for chemical analysis in a range of fields. In ESI, analytes can produce multiple charge states, which must be correctly assigned for identification. Existing approaches to charge state assignment can suffer from limited accuracy and/or poor…
Authors not listed
This paper formally defines an operational isomorphism between spectral damping in molecular vibronic systems and neuromodulatory control in biological sensory systems. Without asserting causal continuity or physical identity across scales, we show that both domains instantiate the same class of output-selective…
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This report compares various simulation and data analysis methods for free induction decay (FID) signals in Nuclear Magnetic Resonance (NMR) Spectroscopy. The methods discussed include discrete fast Fourier transformation (FFT), least squares fitting (LSF), short-time Fourier transformation (STFT), and wavelet…