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25 papers · ranked by Valyu relevance

01
PPubMed8 Sep 2017
Deep Neural Network Probabilistic Decoder for Stabilizer Codes
Stefan Krastanov, Liang Jiang
Neural networks can efficiently encode the probability distribution of errors in an error correcting code. Moreover, these distributions can be conditioned on the syndromes of the corresponding errors. This paves a path forward for a decoder that employs a neural network to calculate the conditional distribution, then…
89relevance
02
PPubMed15 Feb 2019
Efficient neural decoding of self-location with a deep recurrent network Efficient neural decoding of self-location with a deep recurrent network
Ardi Tampuu, Tambet Matiisen, H. Freyja Ólafsdóttir, Caswell Barry + 2 more
Place cells in the mammalian hippocampus signal self-location with sparse spatially stable firing fields. Based on observation of place cell activity it is possible to accurately decode an animal’s location. The precision of this decoding sets a lower bound for the amount of information that the hippocampal population…
85relevance
03
PPubMed12 Nov 2022
Real-time brain-machine interface in non-human primates achieves high-velocity prosthetic finger movements using a shallow feedforward neural network decoder
Matthew S. Willsey, Samuel R. Nason-Tomaszewski, Scott R. Ensel, Hisham Temmar + 4 more
'Hisham Temmar' 'Matthew J. Mender' 'Joseph T. Costello' 'Parag G. Patil' 'Cynthia A. Chestek'] Despite the rapid progress and interest in brain-machine interfaces that restore motor function, the performance of prosthetic fingers and limbs has yet to mimic native function. The algorithm that converts brain signals to…
85relevance
04
BbioRxiv31 Aug 2021
Real-Time Brain-Machine Interface Achieves High-Velocity Prosthetic Finger Movements using a Biologically-Inspired Neural Network Decoder
Matthew S. Willsey, Samuel R. Nason, Scott R. Ensel, Hisham Temmar + 4 more
Despite the rapid progress and interest in brain-machine interfaces that restore motor function, the performance of prosthetic fingers and limbs has yet to mimic native function. The algorithm that converts brain signals to a control signal for the prosthetic device is one of the limitations in achieving rapid and…
84relevance
05
AarXiv2018
High-performance Decoder for Convolutional Code with Deep Neural Network
Xiaobo Jiang, Fang Zhang, Zhen Zeng
The use of deep neural network for decoding error control code will encounter two problems, namely, the high-precision requirements of the error control code and the complexity of the neural network due to the long code. In this paper, a deep neural network decoder is proposed to solve the decoding problem of long code…
83relevance
06
AarXiv2023
Rate Compatible LDPC Neural Decoding Network: A Multi-Task Learning Approach
Yukun Cheng, Wei Chen, Lun Li, Bo Ai
—Deep learning based decoding networks have shown significant improvement in decoding LDPC codes, but the neural decoders are limited by rate-matching operations such as puncturing or extending, thus needing to train multiple decoders with different code rates for a variety of channel conditions. In this…
83relevance
07
PPubMed27 Aug 2020
Machine Learning for Neural Decoding
Joshua I. Glaser, Ari S. Benjamin, Raeed H. Chowdhury, Matthew G. Perich + 2 more
'Matthew G. Perich' 'Lee E. Miller' 'Konrad P. Kording'] Title: Abstract Despite rapid advances in machine learning tools, the majority of neural decoding approaches still use traditional methods. Modern machine learning tools, which are versatile and easy to use, have the potential to significantly improve decoding…
83relevance
08
AarXiv23 Oct 2017
Decoding small surface codes with feedforward neural networks
Savvas Varsamopoulos, Ben Criger, Koen Bertels
Surface codes reach high error thresholds when decoded with known algorithms, but the decoding time will likely exceed the available time budget, especially for near-term implementations. To decrease the decoding time, we reduce the decoding problem to a classification problem that a feedforward neural network can…
81relevance
09
AarXiv1 May 2018
Performance Evaluation of Channel Decoding with Deep Neural Networks
Wei Lyu, Zhaoyang Zhang, Chunxu Jiao, Kangjian Qin + 1 more
—With the demand of high data rate and low latency in fifth generation (5G), deep neural network decoder (NND) has become a promising candidate due to its capability of one-shot decoding and parallel computing. In this paper, three types of NND, i.e., multi-layer perceptron (MLP), convolution neural network (CNN) and…
81relevance
10
AarXiv2022
Neural Network-based OFDM Receiver for Resource Constrained IoT Devices
Nasim Soltani, Hai Cheng, Mauro Belgiovine, Yanyu Li + 9 more
'Bahar Azari' 'Salvatore D’Oro' 'Tales Imbiriba' 'Tommaso Melodia' 'Pau Closas' 'Yanzhi Wang' 'Deniz Erdoğmuş' 'Kaushik Chowdhury'] Abstract—Orthogonal Frequency Division Multiplexing (OFDM)-based waveforms are used for communication links in many current and emerging Internet of Things (IoT) applications, including…
81relevance
11
AarXiv2014
Novel LDPC Decoder via MLP Neural Networks
Alireza Karami, Mahmoud Ahmadian‐Attari
In this paper, a new method for decoding Low Density Parity Check (LDPC) codes, based on Multi-Layer Perceptron (MLP) neural networks is proposed. Due to the fact that in neural networks all procedures are processed in parallel, this method can be considered as a viable alternative to Message Passing Algorithm (MPA)…
79relevance
12
BbioRxiv11 Dec 2019
Interpreting Wide-Band Neural Activity Using Convolutional Neural Networks
Markus Frey, Sander Tanni, Catherine Perrodin, Alice O’Leary + 6 more
Rapid progress in technologies such as calcium imaging and electrophysiology has seen a dramatic increase in the size and extent of neural recordings. Even so, interpretation of this data often depends on manual operations and requires considerable knowledge about the nature of the representation. Decoding provides a…
79relevance
13
AarXiv4 May 2024
Performance Evaluation of PAC Decoding with Deep Neural Networks
Jingxin Dai, Hang Yin, Yansong Lv, Yuhuan Wang + 1 more
—By concatenating a polar transform with a convolutional transform, polarization-adjusted convolutional (PAC) codes can reach the dispersion approximation bound in certain rate cases. However, the sequential decoding nature of traditional PAC decoding algorithms results in high decoding latency. Due to the parallel…
78relevance
14
PPubMed30 Mar 2022
Multi-level pooling encoder–decoder convolution neural network for MRI reconstruction
Sarattha Karnjanapreechakorn, Worapan Kusakunniran, Thanongchai Siriapisith, Pairash Saiviroonporn + 1 more
'Thanongchai Siriapisith' 'Pairash Saiviroonporn' 'Yilun Shang'] MRI reconstruction is one of the critical processes of MRI machines, along with the acquisition. Due to a slow processing time of signal acquiring, parallel imaging and reconstruction techniques are applied for acceleration. To accelerate the acquisition…
78relevance
15
BbioRxiv18 Aug 2023
A Spiking Neural Network with Continuous Local Learning for Robust Online Brain Machine Interface
Elijah A. Taeckens, Sahil Shah
Spiking neural networks (SNNs) are powerful tools that are well suited for brain machine interfaces (BMI) due to their similarity to biological neural systems and computational efficiency. They have shown comparable accuracy to state-of-the-art methods, but current training methods require large amounts of memory, and…
78relevance
16
PPubMed31 Mar 2020
Inhibition of Long-Term Variability in Decoding Forelimb Trajectory Using Evolutionary Neural Networks With Error-Correction Learning
Shih-Hung Yang, Han-Lin Wang, Yu-Chun Lo, Hsin-Yi Lai + 9 more
Objective: In brain machine interfaces (BMIs), the functional mapping between neural activities and kinematic parameters varied over time owing to changes in neural recording conditions. The variability in neural recording conditions might result in unstable long-term decoding performance. Relevant studies trained…
78relevance
17
BbioRxiv5 Mar 2024
Artificial neural network for brain-machine interface consistently produces more naturalistic finger movements than linear methods
Hisham Temmar, Matthew S. Willsey, Joseph T. Costello, Matthew J. Mender + 6 more
Brain-machine interfaces (BMI) aim to restore function to persons living with spinal cord injuries by ‘decoding’ neural signals into behavior. Recently, nonlinear BMI decoders have outperformed previous state-of-the-art linear decoders, but few studies have investigated what specific improvements these nonlinear…
78relevance
18
BbioRxiv10 Nov 2022
A Biologically Plausible Spiking Neural Network for Decoding Kinematics in the Hippocampus and Premotor Cortex
Elijah Taeckens, Ryan Dong, Sahil Shah
This work presents a spiking neural network for predicting kinematics from neural data towards accurate and energy-efficient brain machine interface. A brain machine interface is a technological system that interprets neural signals to allow motor impaired patients to control prosthetic devices. Spiking neural networks…
77relevance
19
BbioRxiv10 Aug 2020
Deep Recurrent Neural Network and Point Process Filter Approaches in Multidimensional Neural Decoding Problems
Mohammad Reza Rezaei, Behzad Nazari, Saeid Sadri, Ali Yousefi
Recent technological and experimental advances in recording from neural systems have led to a significant increase in the type and volume of data being collected in neuroscience experiments. This brings an increasing demand for development of appropriate analytical tools to analyze large scale neuroscience data.…
75relevance
20
CchemRxiv5 May 2025
Obstacle detection during Navigation using Convolutional Neural Networks with LSTM
Authors not listed
This research presents a novel approach to obstacle detection during navigation using a combination of Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks. The primary objective is to generate accurate image captions that describe the content of images, which is crucial for applications such…
72relevance
21
CchemRxiv18 Apr 2023
Construction of order-independent molecular fragments space with vector quantised graph autoencoder
Tagir Akhmetshin, Arkadii Lin, Timur Madzhidov, Alexandre Varnek
Autoencoders represent a promising technique for the inverse quantitative structure-activity relationship (QSAR) task. However, undesirable bias, such as atom ordering, affects the neighbourhood behaviour of autoencoders’ latent space and, consequently, usage of the latent vectors as variables in machine-learning…
67relevance
22
CchemRxiv11 Mar 2024
Advancements in Hand-Drawn Chemical Structure Recognition through an Enhanced DECIMER Architecture
Kohulan Rajan, Henning Otto Brinkhaus, Achim Zielesny, Christoph Steinbeck
Accurate recognition of hand-drawn chemical structures is crucial for digitising hand-written chemical information found in traditional laboratory notebooks or for facilitating stylus-based structure entry on tablets or smartphones. However, the inherent variability in hand-drawn structures poses challenges for…
66relevance
23
CchemRxiv17 Apr 2025
A Fast Neural Network for Isotopic Charge State Assignment
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…
65relevance
24
CchemRxiv18 May 2022
Multi-fidelity machine learning models for improved high-throughput screening predictions
David Buterez, Jon Paul Janet, Steven Kiddle, Pietro Liò
We investigate the potential of graph neural networks for transfer learning and improving molecular property prediction on sparse and expensive to acquire high-fidelity data by leveraging low-fidelity measurements as an inexpensive proxy for a targeted property ofinterest. This problem arises in discovery processes…
63relevance
25
CchemRxiv13 Jun 2023
Ultra-sparse View X-ray Computed Tomography for 4-D Imaging
Kelsey Hatzell, Yanjie Zheng
X-ray Computed Tomography (CT) is a non-invasive, non-destructive approach to imaging materials, material systems and engineered components in two- and three- dimensions. Acquisition of 3D images requires the collection of hundreds or thousands of through-thickness X-ray radiographic images from different angles. Such…
56relevance

Results for neural network decoder

Deep Neural Network Probabilistic Decoder for Stabilizer Codes

Efficient neural decoding of self-location with a deep recurrent network Efficient neural decoding of self-location with a deep recurrent network

Real-time brain-machine interface in non-human primates achieves high-velocity prosthetic finger movements using a shallow feedforward neural network decoder

Real-Time Brain-Machine Interface Achieves High-Velocity Prosthetic Finger Movements using a Biologically-Inspired Neural Network Decoder

High-performance Decoder for Convolutional Code with Deep Neural Network

Rate Compatible LDPC Neural Decoding Network: A Multi-Task Learning Approach

Machine Learning for Neural Decoding

Decoding small surface codes with feedforward neural networks

Performance Evaluation of Channel Decoding with Deep Neural Networks

Neural Network-based OFDM Receiver for Resource Constrained IoT Devices

Novel LDPC Decoder via MLP Neural Networks

Interpreting Wide-Band Neural Activity Using Convolutional Neural Networks

Performance Evaluation of PAC Decoding with Deep Neural Networks

Multi-level pooling encoder–decoder convolution neural network for MRI reconstruction

A Spiking Neural Network with Continuous Local Learning for Robust Online Brain Machine Interface

Inhibition of Long-Term Variability in Decoding Forelimb Trajectory Using Evolutionary Neural Networks With Error-Correction Learning

Artificial neural network for brain-machine interface consistently produces more naturalistic finger movements than linear methods

A Biologically Plausible Spiking Neural Network for Decoding Kinematics in the Hippocampus and Premotor Cortex

Deep Recurrent Neural Network and Point Process Filter Approaches in Multidimensional Neural Decoding Problems

Obstacle detection during Navigation using Convolutional Neural Networks with LSTM

Construction of order-independent molecular fragments space with vector quantised graph autoencoder

Advancements in Hand-Drawn Chemical Structure Recognition through an Enhanced DECIMER Architecture

A Fast Neural Network for Isotopic Charge State Assignment

Multi-fidelity machine learning models for improved high-throughput screening predictions

Ultra-sparse View X-ray Computed Tomography for 4-D Imaging

Deep Neural Network Probabilistic Decoder for Stabilizer Codes

Efficient neural decoding of self-location with a deep recurrent network Efficient neural decoding of self-location with a deep recurrent network

Real-time brain-machine interface in non-human primates achieves high-velocity prosthetic finger movements using a shallow feedforward neural network decoder

Real-Time Brain-Machine Interface Achieves High-Velocity Prosthetic Finger Movements using a Biologically-Inspired Neural Network Decoder

High-performance Decoder for Convolutional Code with Deep Neural Network

Rate Compatible LDPC Neural Decoding Network: A Multi-Task Learning Approach

Machine Learning for Neural Decoding

Decoding small surface codes with feedforward neural networks

Performance Evaluation of Channel Decoding with Deep Neural Networks

Neural Network-based OFDM Receiver for Resource Constrained IoT Devices

Novel LDPC Decoder via MLP Neural Networks

Interpreting Wide-Band Neural Activity Using Convolutional Neural Networks

Performance Evaluation of PAC Decoding with Deep Neural Networks

Multi-level pooling encoder–decoder convolution neural network for MRI reconstruction

A Spiking Neural Network with Continuous Local Learning for Robust Online Brain Machine Interface

Inhibition of Long-Term Variability in Decoding Forelimb Trajectory Using Evolutionary Neural Networks With Error-Correction Learning

Artificial neural network for brain-machine interface consistently produces more naturalistic finger movements than linear methods

A Biologically Plausible Spiking Neural Network for Decoding Kinematics in the Hippocampus and Premotor Cortex

Deep Recurrent Neural Network and Point Process Filter Approaches in Multidimensional Neural Decoding Problems

Obstacle detection during Navigation using Convolutional Neural Networks with LSTM

Construction of order-independent molecular fragments space with vector quantised graph autoencoder

Advancements in Hand-Drawn Chemical Structure Recognition through an Enhanced DECIMER Architecture

A Fast Neural Network for Isotopic Charge State Assignment

Multi-fidelity machine learning models for improved high-throughput screening predictions

Ultra-sparse View X-ray Computed Tomography for 4-D Imaging