{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,7]],"date-time":"2026-04-07T20:41:59Z","timestamp":1775594519820,"version":"3.50.1"},"reference-count":55,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2024,4,12]],"date-time":"2024-04-12T00:00:00Z","timestamp":1712880000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Institute for Infocomm Research (I2R)","award":["EC-2015-216"],"award-info":[{"award-number":["EC-2015-216"]}]},{"name":"Agency for Science, Technology and Research (A*STAR), Singapore","award":["EC-2015-216"],"award-info":[{"award-number":["EC-2015-216"]}]},{"DOI":"10.13039\/501100001348","name":"Robust Neural Decoding and Control System","doi-asserted-by":"publisher","award":["EC-2015-216"],"award-info":[{"award-number":["EC-2015-216"]}],"id":[{"id":"10.13039\/501100001348","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Singapore International Pre-Graduate Award (SIPGA), by A*STAR Graduate Academy (A*GA)","award":["EC-2015-216"],"award-info":[{"award-number":["EC-2015-216"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>Background. Brain\u2013machine interfaces (BMIs) offer users the ability to directly communicate with digital devices through neural signals decoded with machine learning (ML)-based algorithms. Spiking Neural Networks (SNNs) are a type of Artificial Neural Network (ANN) that operate on neural spikes instead of continuous scalar outputs. Compared to traditional ANNs, SNNs perform fewer computations, use less memory, and mimic biological neurons better. However, SNNs only retain information for short durations, limiting their ability to capture long-term dependencies in time-variant data. Here, we propose a novel spike-weighted SNN with spiking long short-term memory (swSNN-SLSTM) for a regression problem. Spike-weighting captures neuronal firing rate instead of membrane potential, and the SLSTM layer captures long-term dependencies. Methods. We compared the performance of various ML algorithms during decoding directional movements, using a dataset of microelectrode recordings from a macaque during a directional joystick task, and also an open-source dataset. We thus quantified how swSNN-SLSTM performed compared to existing ML models: an unscented Kalman filter, LSTM-based ANN, and membrane-based SNN techniques. Result. The proposed swSNN-SLSTM outperforms both the unscented Kalman filter, the LSTM-based ANN, and the membrane based SNN technique. This shows that incorporating SLSTM can better capture long-term dependencies within neural data. Also, our proposed swSNN-SLSTM algorithm shows promise in reducing power consumption and lowering heat dissipation in implanted BMIs.<\/jats:p>","DOI":"10.3390\/a17040156","type":"journal-article","created":{"date-parts":[[2024,4,12]],"date-time":"2024-04-12T09:28:06Z","timestamp":1712914086000},"page":"156","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Spike-Weighted Spiking Neural Network with Spiking Long Short-Term Memory: A Biomimetic Approach to Decoding Brain Signals"],"prefix":"10.3390","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-4120-859X","authenticated-orcid":false,"given":"Kyle","family":"McMillan","sequence":"first","affiliation":[{"name":"Department of Engineering, University of Cambridge, Trumpington Street, Cambridge CB2 1PZ, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rosa Qiyue","family":"So","sequence":"additional","affiliation":[{"name":"Institute for Infocomm Research (I2R), Agency for Science, Technology and Research (A*STAR), 1 Fusionopolis Way, #21-01 Connexis (South Tower), Singapore 138632, Singapore"},{"name":"Department of Biomedical Engineering, National University of Singapore, Singapore 117583, Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Camilo","family":"Libedinsky","sequence":"additional","affiliation":[{"name":"Department of Psychology, National University of Singapore, Singapore 117570, Singapore"},{"name":"Institute of Molecular and Cell Biology (IMCB), Agency for Science, Technology and Research (A*STAR), 61 Biopolis Drive, Proteos, Singapore 138673, Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3053-6311","authenticated-orcid":false,"given":"Kai Keng","family":"Ang","sequence":"additional","affiliation":[{"name":"Institute for Infocomm Research (I2R), Agency for Science, Technology and Research (A*STAR), 1 Fusionopolis Way, #21-01 Connexis (South Tower), Singapore 138632, Singapore"},{"name":"School of Computer Science and Engineering, Nanyang Technological University, Singapore 639798, Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3373-8362","authenticated-orcid":false,"given":"Brian","family":"Premchand","sequence":"additional","affiliation":[{"name":"Institute for Infocomm Research (I2R), Agency for Science, Technology and Research (A*STAR), 1 Fusionopolis Way, #21-01 Connexis (South Tower), Singapore 138632, Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,4,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"3067","DOI":"10.1152\/jn.01038.2011","article-title":"State-Based Decoding of Hand and Finger Kinematics Using Neuronal Ensemble and LFP Activity during Dexterous Reach-to-Grasp Movements","volume":"109","author":"Aggarwal","year":"2013","journal-title":"J. Neurophysiol."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Carmena, J.M., Lebedev, M.A., Crist, R.E., O\u2019Doherty, J.E., Santucci, D.M., Dimitrov, D.F., Patil, P.G., Henriquez, C.S., and Nicolelis, M.A.L. (2003). Learning to Control a Brain\u2013Machine Interface for Reaching and Grasping by Primates. PLoS Biol., 1.","DOI":"10.1371\/journal.pbio.0000042"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"So, R., Xu, Z., Libedinsky, C., Toe, K.K., Ang, K.K., Yen, S.-C., and Guan, C. (2015, January 22\u201324). Neural Representations of Movement Intentions during Brain-Controlled Self-Motion. Proceedings of the 2015 7th International IEEE\/EMBS Conference on Neural Engineering (NER), Montpellier, France.","DOI":"10.1109\/NER.2015.7146601"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"036006","DOI":"10.1088\/1741-2552\/accf36","article-title":"Restoring Continuous Finger Function with Temporarily Paralyzed Nonhuman Primates Using Brain\u2013Machine Interfaces","volume":"20","author":"Mender","year":"2023","journal-title":"J. Neural Eng."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"026007","DOI":"10.1088\/1741-2552\/aa9ee7","article-title":"Rapid Calibration of an Intracortical Brain\u2013Computer Interface for People with Tetraplegia","volume":"15","author":"Brandman","year":"2018","journal-title":"J. Neural Eng."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"172","DOI":"10.1016\/j.nbd.2014.11.025","article-title":"Brain\u2013Machine Interfaces in Neurorehabilitation of Stroke","volume":"83","author":"Soekadar","year":"2015","journal-title":"Neurobiol. Dis."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1115\/1.3662552","article-title":"A New Approach to Linear Filtering and Prediction Problems","volume":"82","author":"Kalman","year":"1960","journal-title":"J. Basic Eng."},{"key":"ref_8","unstructured":"Chen, Z., and Takahashi, K. (2013, January 3\u20137). Sparse Bayesian Inference Methods for Decoding 3D Reach and Grasp Kinematics and Joint Angles with Primary Motor Cortical Ensembles. Proceedings of the 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Osaka, Japan."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Li, Z., O\u2019Doherty, J.E., Hanson, T.L., Lebedev, M.A., Henriquez, C.S., and Nicolelis, M.A.L. (2009). Unscented Kalman Filter for Brain-Machine Interfaces. PLoS ONE, 4.","DOI":"10.1371\/journal.pone.0006243"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Dangi, S., Gowda, S., H\u00e9liot, R., and Carmena, J.M. (May, January 27). Adaptive Kalman Filtering for Closed-Loop Brain-Machine Interface Systems. Proceedings of the 2011 5th International IEEE\/EMBS Conference on Neural Engineering, Cancun, Mexico.","DOI":"10.1109\/NER.2011.5910622"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Homer, M.L., Harrison, M.T., Black, M.J., Perge, J.A., Cash, S.S., Friehs, G., and Hochberg, L.R. (2013, January 6\u20138). Mixing Decoded Cursor Velocity and Position from an Offline Kalman Filter Improves Cursor Control in People with Tetraplegia. Proceedings of the 2013 6th International IEEE\/EMBS Conference on Neural Engineering (NER), San Diego, CA, USA.","DOI":"10.1109\/NER.2013.6696034"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"220","DOI":"10.3389\/fnbeh.2019.00220","article-title":"Neural Interactions in a Spatially-Distributed Cortical Network During Perceptual Decision-Making","volume":"13","author":"Maksimenko","year":"2019","journal-title":"Front. Behav. Neurosci."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"587","DOI":"10.3389\/fnins.2016.00587","article-title":"An Improved Unscented Kalman Filter Based Decoder for Cortical Brain-Machine Interfaces","volume":"10","author":"Li","year":"2016","journal-title":"Front. Neurosci."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1085","DOI":"10.1162\/neco_a_01189","article-title":"Decoding Movements from Cortical Ensemble Activity Using a Long Short-Term Memory Recurrent Network","volume":"31","author":"Tseng","year":"2019","journal-title":"Neural Comput."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Hosman, T., Vilela, M., Milstein, D., Kelemen, J.N., Brandman, D.M., Hochberg, L.R., and Simeral, J.D. (2019, January 20\u201323). BCI Decoder Performance Comparison of an LSTM Recurrent Neural Network and a Kalman Filter in Retrospective Simulation. Proceedings of the 2019 9th International IEEE\/EMBS Conference on Neural Engineering (NER), San Francisco, CA, USA.","DOI":"10.1109\/NER.2019.8717140"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Premchand, B., Toe, K.K., Wang, C., Shaikh, S., Libedinsky, C., Ang, K.K., and So, R.Q. (2020, January 20\u201324). Decoding Movement Direction from Cortical Microelectrode Recordings Using an LSTM-Based Neural Network. Proceedings of the 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), Montreal, QC, Canada.","DOI":"10.1109\/EMBC44109.2020.9175593"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"797","DOI":"10.1109\/TIM.2007.913724","article-title":"Energy Consumption Estimation in Embedded Systems","volume":"57","author":"Konstantakos","year":"2008","journal-title":"Instrum. Meas. IEEE Trans."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"2295","DOI":"10.1109\/JPROC.2017.2761740","article-title":"Efficient Processing of Deep Neural Networks: A Tutorial and Survey","volume":"105","author":"Sze","year":"2017","journal-title":"Proc. IEEE"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Reichert, W.M. (2008). Indwelling Neural Implants: Strategies for Contending with the In Vivo Environment, CRC Press\/Taylor & Francis.","DOI":"10.1201\/9781420009309"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1142\/S0129065709002002","article-title":"Spiking Neural Networks","volume":"19","author":"Adeli","year":"2009","journal-title":"Int. J. Neural Syst."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"617","DOI":"10.1016\/S0893-6080(01)00067-3","article-title":"Building Blocks for Electronic Spiking Neural Networks","volume":"14","year":"2001","journal-title":"Neural Netw."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Han, B., Sengupta, A., and Roy, K. (2016, January 24\u201329). On the Energy Benefits of Spiking Deep Neural Networks: A Case Study. Proceedings of the 2016 International Joint Conference on Neural Networks (IJCNN), Vancouver, BC, Canada.","DOI":"10.1109\/IJCNN.2016.7727303"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Henkes, A., Eshraghian, J.K., and Wessels, H. (2022). Spiking Neural Networks for Nonlinear Regression. arXiv.","DOI":"10.29363\/nanoge.neumatdecas.2023.045"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"662","DOI":"10.3389\/fnins.2020.00662","article-title":"Optimizing the Energy Consumption of Spiking Neural Networks for Neuromorphic Applications","volume":"14","author":"Sorbaro","year":"2020","journal-title":"Front. Neurosci."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"331","DOI":"10.3389\/fnins.2018.00331","article-title":"Spatio-Temporal Backpropagation for Training High-Performance Spiking Neural Networks","volume":"12","author":"Wu","year":"2018","journal-title":"Front. Neurosci."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1186\/s13640-015-0059-4","article-title":"Simplified Spiking Neural Network Architecture and STDP Learning Algorithm Applied to Image Classification","volume":"2015","author":"Iakymchuk","year":"2015","journal-title":"EURASIP J. Image Video Process."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Liao, J., Widmer, L., Wang, X., Di Mauro, A., Nason-Tomaszewski, S.R., Chestek, C.A., Benini, L., and Jang, T. (2022, January 13\u201315). An Energy-Efficient Spiking Neural Network for Finger Velocity Decoding for Implantable Brain-Machine Interface. Proceedings of the 2022 IEEE 4th International Conference on Artificial Intelligence Circuits and Systems (AICAS), Incheon, Republic of Korea.","DOI":"10.1109\/AICAS54282.2022.9869846"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Nason, S.R., Mender, M.J., Vaskov, A.K., Willsey, M.S., Ganesh Kumar, N., Kung, T.A., Patil, P.G., and Chestek, C.A. (2021). Example Data and Code for \u201cReal-Time Linear Prediction of Simultaneous and Independent Movements of Two Finger Groups Using an Intracortical Brain-Machine Interface\u201d, University of Michigan.","DOI":"10.1101\/2020.10.27.357228"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"3164","DOI":"10.1016\/j.neuron.2021.08.009","article-title":"Real-Time Linear Prediction of Simultaneous and Independent Movements of Two Finger Groups Using an Intracortical Brain-Machine Interface","volume":"109","author":"Nason","year":"2021","journal-title":"Neuron"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Libedinsky, C., So, R., Xu, Z., Kyar, T.K., Ho, D., Lim, C., Chan, L., Chua, Y., Yao, L., and Cheong, J.H. (2016). Independent Mobility Achieved through a Wireless Brain-Machine Interface. PLoS ONE, 11.","DOI":"10.1371\/journal.pone.0165773"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Yang, H., Libedinsky, C., Guan, C., Ang, K., and So, R. (2017, January 11\u201315). Boosting Performance in Brain-Machine Interface by Classifier-Level Fusion Based on Accumulative Training Models from Multi-Day Data. Proceedings of the 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Jeju, Republic of Korea.","DOI":"10.1109\/EMBC.2017.8037224"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Premchand, B., Toe, K.K., Wang, C.C., Libedinsky, C., Ang, K.K., and So, R.Q. (2019, January 19\u201322). Rapid Detection of Inactive Channels during Multi-Unit Intracranial Recordings. Proceedings of the 2019 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI), Chicago, IL, USA.","DOI":"10.1109\/BHI.2019.8834654"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1679","DOI":"10.1152\/jn.90989.2008","article-title":"Using a Common Average Reference to Improve Cortical Neuron Recordings From Microelectrode Arrays","volume":"101","author":"Ludwig","year":"2009","journal-title":"J. Neurophysiol."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1016","DOI":"10.1109\/JPROC.2023.3308088","article-title":"Training Spiking Neural Networks Using Lessons From Deep Learning","volume":"111","author":"Eshraghian","year":"2023","journal-title":"Proc. IEEE"},{"key":"ref_35","unstructured":"Rathi, N., Srinivasan, G., Panda, P., and Roy, K. (2020). Enabling Deep Spiking Neural Networks with Hybrid Conversion and Spike Timing Dependent Backpropagation. arXiv."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Masquelier, T., and Thorpe, S.J. (2007). Unsupervised Learning of Visual Features through Spike Timing Dependent Plasticity. PLoS Comput. Biol., 3.","DOI":"10.1371\/journal.pcbi.0030031"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"2451","DOI":"10.1162\/089976600300015015","article-title":"Learning to Forget: Continual Prediction with LSTM","volume":"12","author":"Gers","year":"2000","journal-title":"Neural Comput."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Tan, P.-Y., Wu, C.-W., and Lu, J.-M. (2021, January 1\u20135). An Improved STBP for Training High-Accuracy and Low-Spike-Count Spiking Neural Networks. Proceedings of the 2021 Design, Automation & Test in Europe Conference & Exhibition (DATE), Virtual.","DOI":"10.23919\/DATE51398.2021.9474151"},{"key":"ref_39","first-page":"21056","article-title":"Deep Residual Learning in Spiking Neural Networks","volume":"Volume 34","author":"Fang","year":"2021","journal-title":"Proceedings of the 34th Conference on Advances in Neural Information Processing Systems"},{"key":"ref_40","unstructured":"Hinton, G.E., Srivastava, N., Krizhevsky, A., Sutskever, I., and Salakhutdinov, R.R. (2012). Improving Neural Networks by Preventing Co-Adaptation of Feature Detectors. arXiv."},{"key":"ref_41","unstructured":"Kingma, D.P., and Ba, J. (2017). Adam: A Method for Stochastic Optimization. arXiv."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"026011","DOI":"10.1088\/1741-2552\/abde8a","article-title":"Robust and Accurate Decoding of Hand Kinematics from Entire Spiking Activity Using Deep Learning","volume":"18","author":"Ahmadi","year":"2021","journal-title":"J. Neural Eng."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"1752","DOI":"10.1038\/nn.3265","article-title":"A High-Performance Neural Prosthesis Enabled by Control Algorithm Design","volume":"15","author":"Gilja","year":"2012","journal-title":"Nat. Neurosci."},{"key":"ref_44","unstructured":"Wu, W., Black, M., Gao, Y., Serruya, M., Shaikhouni, A., Donoghue, J., and Bienenstock, E. (2002). Advances in Neural Information Processing Systems, MIT Press."},{"key":"ref_45","unstructured":"(2023, September 02). Frontiers|Deep Learning with Spiking Neurons: Opportunities and Challenges. Available online: https:\/\/www.frontiersin.org\/articles\/10.3389\/fnins.2018.00774\/full."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"57967","DOI":"10.1109\/ACCESS.2020.2982416","article-title":"Recurrent Neural Networks: An Embedded Computing Perspective","volume":"8","author":"Rezk","year":"2020","journal-title":"IEEE Access"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Horowitz, M. (2014, January 9\u201313). 1.1 Computing\u2019s Energy Problem (and What We Can Do about It). Proceedings of the 2014 IEEE International Solid-State Circuits Conference Digest of Technical Papers (ISSCC), San Francisco, CA, USA.","DOI":"10.1109\/ISSCC.2014.6757323"},{"key":"ref_48","first-page":"761","article-title":"Two Efficient Implementation Forms of Unscented Kalman Filter","volume":"39","author":"Zhang","year":"2011","journal-title":"Control Intell. Syst."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"411","DOI":"10.1152\/jn.00391.2013","article-title":"Motor Cortical Control of Movement Speed with Implications for Brain-Machine Interface Control","volume":"112","author":"Golub","year":"2014","journal-title":"J. Neurophysiol."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Eshraghian, J.K., Lammie, C., Azghadi, M.R., and Lu, W.D. (2022, January 13\u201315). Navigating Local Minima in Quantized Spiking Neural Networks. Proceedings of the 2022 IEEE 4th International Conference on Artificial Intelligence Circuits and Systems (AICAS), Incheon, Republic of Korea.","DOI":"10.1109\/AICAS54282.2022.9869966"},{"key":"ref_51","unstructured":"Buhrmester, V., M\u00fcnch, D., and Arens, M. (2019). Analysis of Explainers of Black Box Deep Neural Networks for Computer Vision: A Survey. arXiv."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Premchand, B., Toe, K.K., Wang, C., Libedinsky, C., Ang, K.K., and So, R.Q. (2022, January 11\u201315). Information Sparseness in Cortical Microelectrode Channels While Decoding Movement Direction Using an Artificial Neural Network. Proceedings of the 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), Glasgow, UK.","DOI":"10.1109\/EMBC48229.2022.9870896"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"481","DOI":"10.1016\/j.conb.2004.07.007","article-title":"Sparse Coding of Sensory Inputs","volume":"14","author":"Olshausen","year":"2004","journal-title":"Curr. Opin. Neurobiol."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"1032","DOI":"10.1038\/s41928-023-01069-w","article-title":"Brain Organoid Reservoir Computing for Artificial Intelligence","volume":"6","author":"Cai","year":"2023","journal-title":"Nat. Electron."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"6899","DOI":"10.1038\/s41467-022-34452-w","article-title":"Real-Time Brain-Machine Interface in Non-Human Primates Achieves High-Velocity Prosthetic Finger Movements Using a Shallow Feedforward Neural Network Decoder","volume":"13","author":"Willsey","year":"2022","journal-title":"Nat. Commun."}],"container-title":["Algorithms"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-4893\/17\/4\/156\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T14:27:03Z","timestamp":1760106423000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-4893\/17\/4\/156"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,4,12]]},"references-count":55,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2024,4]]}},"alternative-id":["a17040156"],"URL":"https:\/\/doi.org\/10.3390\/a17040156","relation":{},"ISSN":["1999-4893"],"issn-type":[{"value":"1999-4893","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,4,12]]}}}