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Meta-Learning for decoding neural Activity Data With Noisy Labels

FRONTIERS IN COMPUTATIONAL NEUROSCIENCE 2022年 16卷 913617页

作者： Xu, Dongfang Chen, Rong Univ Maryland Sch Med Dept Diagnost Radiol & Nucl Med Baltimore MD 21201 USA

In neural decoding, a behavioral variable is often generated by manual annotation and the annotated labels could contain extensive label noise, leading to poor model generalizability. Tackling the label noise problem in neural decoding can improve model generalizability and robustness. We use a deep neural network based sample reweighting method to tackle this problem. The proposed method reweights training samples by using a small and clean validation dataset to guide learning. We evaluated the sample reweighting method on simulated neural activity data and calcium imaging data of anterior lateral motor cortex. For the simulated data, the proposed method can accurately predict the behavioral variable even in the scenario that 36 percent of samples in the training dataset are mislabeled. For the anterior lateral motor cortex study, the proposed method can predict trial types with F1 score of around 0.85 even 48 percent of training samples are mislabeled.

关键词： neural decoding noisy label sample reweighting method deep neural networks anterior lateral motor cortex

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decoding of cortex-wide brain activity from local recordings of neural potentials

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JOURNAL OF neural ENGINEERING 2021年第6期18卷 066009-066009页

作者： Liu, Xin Ren, Chi Huang, Zhisheng Wilson, Madison Kim, Jeong-Hoon Lu, Yichen Ramezani, Mehrdad Komiyama, Takaki Kuzum, Duygu Univ Calif San Diego Dept Elect & Comp Engn La Jolla CA 92093 USA Univ Calif San Diego Div Biol Sci Neurobiol Sect La Jolla CA 92093 USA Univ Calif San Diego Ctr Neural Circuits & Behav La Jolla CA 92093 USA Univ Calif San Diego Dept Neurosci La Jolla CA 92093 USA Univ Calif San Diego Halicioglu Data Sci Inst La Jolla CA 92093 USA

Objective. Electrical recordings of neural activity from brain surface have been widely employed in basic neuroscience research and clinical practice for investigations of neural circuit functions, brain-computer interfaces, and treatments for neurological disorders. Traditionally, these surface potentials have been believed to mainly reflect local neural activity. It is not known how informative the locally recorded surface potentials are for the neural activities across multiple cortical regions. Approach. To investigate that, we perform simultaneous local electrical recording and wide-field calcium imaging in awake head-fixed mice. Using a recurrent neural network model, we try to decode the calcium fluorescence activity of multiple cortical regions from local electrical recordings. Main results. The mean activity of different cortical regions could be decoded from locally recorded surface potentials. Also, each frequency band of surface potentials differentially encodes activities from multiple cortical regions so that including all the frequency bands in the decoding model gives the highest decoding performance. Despite the close spacing between recording channels, surface potentials from different channels provide complementary information about the large-scale cortical activity and the decoding performance continues to improve as more channels are included. Finally, we demonstrate the successful decoding of whole dorsal cortex activity at pixel-level using locally recorded surface potentials. Significance. These results show that the locally recorded surface potentials indeed contain rich information of the large-scale neural activities, which could be further demixed to recover the neural activity across individual cortical regions. In the future, our cross-modality inference approach could be adapted to virtually reconstruct cortex-wide brain activity, greatly expanding the spatial reach of surface electrical recordings without increasing invasiveness. Furt

关键词： electrophysiology wide-field imaging neural network large-scale brain activity neural decoding

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Olfactory decoding Method Using neural Spike Signals

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Journal of Measurement Science and Instrumentation 2010年第1期1卷 81-85页

作者： Kyung-jin YOU Hyun-chool SHIN Dept.of Electronic Engineering Soongsil UniversitySeoul 156-743Korea

This paper presents a novel method for inferring the odor based on neural activities observed from rats' main olfactory ***-channel extra-cellular single unit recordings are done by micro-wire electrodes(Tungsten,50 μm,32 channels)implanted in the mitral/tufted cell layers of the main olfactory bulb of the anesthetized rats to obtain neural responses to various *** responses as a key feature are measured by subtraction firing rates before stimulus from *** odor inference,a decoding method is developed based on the ML *** results show that the average decoding accuracy is about 100.0%,96.0%,and 80.0% with three rats,*** work has profound implications for a novel brain-machine interface system for odor inference.

关键词： olfactory odoronts inference neural decoding neural signal processing neural activity

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neural activity underlying the detection of an object movement by an observer during forward self-motion: Dynamic decoding and temporal evolution of directional cortical connectivity

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PROGRESS IN NEUROBIOLOGY 2020年第0期195卷 101824-101824页

作者： Kozhemiako, N. Nunes, A. S. Samal, A. Rana, K. D. Calabro, F. J. Hamalainen, M. S. Khan, S. Vaina, L. M. Simon Fraser Univ Dept Biomed Physiol & Kinesiol Burnaby BC Canada Massachusetts Gen Hosp Dept Radiol Athinoula A Martinos Ctr Biomed Imaging Charlestown MA USA Boston Univ Dept Biomed Engn 44 Cummington Mall Boston MA 02215 USA Boston Univ Dept Neurol 44 Cummington Mall Boston MA 02215 USA Boston Univ Grad Program Neurosci 44 Cummington Mall Boston MA 02215 USA NIMH Bethesda MD 20892 USA Univ Pittsburgh Dept Psychiat & Biomed Engn Pittsburgh PA 15260 USA Harvard Med Sch Boston MA 02115 USA

Relatively little is known about how the human brain identifies movement of objects while the observer is also moving in the environment. This is, ecologically, one of the most fundamental motion processing problems, critical for survival. To study this problem, we used a task which involved nine textured spheres moving in depth, eight simulating the observer's forward motion while the ninth, the target, moved independently with a different speed towards or away from the observer. Capitalizing on the high temporal resolution of magnetoencephalography (MEG) we trained a Support Vector Classifier (SVC) using the sensor-level data to identify correct and incorrect responses. Using the same MEG data, we addressed the dynamics of cortical processes involved in the detection of the independently moving object and investigated whether we could obtain confirmatory evidence for the brain activity patterns used by the classifier. Our findings indicate that response correctness could be reliably predicted by the SVC, with the highest accuracy during the blank period after motion and preceding the response. The spatial distribution of the areas critical for the correct prediction was similar but not exclusive to areas underlying the evoked activity. Importantly, SVC identified frontal areas otherwise not detected with evoked activity that seem to be important for the successful performance in the task. Dynamic connectivity further supported the involvement of frontal and occipital-temporal areas during the task periods. This is the first study to dynamically map cortical areas using a fully data-driven approach in order to investigate the neural mechanisms involved in the detection of moving objects during observer's self-motion.

关键词： Optic flow Flow parsing Magnetoencephalography neural decoding Dynamic Granger causality

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decoding speech from spike-based neural population recordings in secondary auditory cortex of non-human primates

COMMUNICATIONS BIOLOGY

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COMMUNICATIONS BIOLOGY 2019年第1期2卷 466页

作者： Heelan, Christopher Lee, Jihun O'Shea, Ronan Lynch, Laurie Brandman, David M. Truccolo, Wilson Nurmikko, Arto, V Brown Univ Sch Engn Providence RI 02912 USA Connexon Syst Providence RI 02912 USA Dalhousie Univ Dept Surg Neurosurg Halifax NS Canada Brown Univ Dept Neurosci Providence RI 02912 USA Brown Univ Carney Inst Brain Sci Providence RI 02912 USA

Direct electronic communication with sensory areas of the neocortex is a challenging ambition for brain-computer interfaces. Here, we report the first successful neural decoding of English words with high intelligibility from intracortical spike-based neural population activity recorded from the secondary auditory cortex of macaques. We acquired 96-channel full-broadband population recordings using intracortical microelectrode arrays in the rostral and caudal parabelt regions of the superior temporal gyrus (STG). We leveraged a new neural processing toolkit to investigate the choice of decoding algorithm, neural preprocessing, audio representation, channel count, and array location on neural decoding performance. The presented spike-based machine learning neural decoding approach may further be useful in informing future encoding strategies to deliver direct auditory percepts to the brain as specific patterns of microstimulation.

关键词： Cortex neural decoding

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

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FRONTIERS IN COMPUTATIONAL NEUROSCIENCE 2020年 14卷 22页

作者： Yang, Shih-Hung Wang, Han-Lin Lo, Yu-Chun Lai, Hsin-Yi Chen, Kuan-Yu Lan, Yu-Hao Kao, Ching-Chia Chou, Chin Lin, Sheng-Huang Huang, Jyun-We Wang, Ching-Fu Kuo, Chao-Hung Chen, You-Yin Natl Cheng Kung Univ Dept Mech Engn Tainan Taiwan Natl Yang Ming Univ Dept Biomed Engn Taipei Taiwan Taipei Med Univ Coll Med Sci & Technol Ph Program Neural Regenerat Med Taipei Taiwan Zhejiang Univ Interdisciplinary Inst Neurosci & Technol Key Lab Med Neurobiol Zhejiang Prov Sch MedDept NeurolAffiliated Hosp 2 Hangzhou Peoples R China Zhejiang Univ Qiushi Acad Adv Studies Coll Biomed Engn & Instrument Sci Key Lab Biomed EngnMinist Educ Hangzhou Peoples R China Acad Sinica Res Ctr Informat Technol Innovat Taipei Taiwan Univ Southern Calif Dept Regulatory & Qual Sci Los Angeles CA 90007 USA Hualien Tzu Chi Hosp Dept Neurol Buddhist Tzu Chi Med Fdn Hualien Taiwan Tzu Chi Univ Sch Med Dept Neurol Hualien Taiwan Taipei Vet Gen Hosp Neurol Inst Dept Neurosurg Taipei Taiwan Univ Washington Dept Neurol Surg Seattle WA 98195 USA

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 decoders with several days of training data to make them inherently robust to changes in neural recording conditions. However, these decoders might not be robust to changes in neural recording conditions when only a few days of training data are available. In time-series prediction and feedback control system, an error feedback was commonly adopted to reduce the effects of model uncertainty. This motivated us to introduce an error feedback to a neural decoder for dealing with the variability in neural recording conditions. Approach: We proposed an evolutionary constructive and pruning neural network with error feedback (ECPNN-EF) as a neural decoder. The ECPNN-EF with partially connected topology decoded the instantaneous firing rates of each sorted unit into forelimb movement of a rat. Furthermore, an error feedback was adopted as an additional input to provide kinematic information and thus compensate for changes in functional mapping. The proposed neural decoder was trained on data collected from a water reward-related lever-pressing task for a rat. The first 2 days of data were used to train the decoder, and the subsequent 10 days of data were used to test the decoder. Main Results: The ECPNN-EF under different settings was evaluated to better understand the impact of the error feedback and partially connected topology. The experimental results demonstrated that the ECPNN-EF achieved significantly higher daily decoding performance with smaller daily variability when using the error feedback and partially connected topology.

关键词： brain machine interfaces neural decoding error feedback evolutionary algorithm recurrent neural network

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neural Representation Precision of Distance Predicts Children's Arithmetic Performance

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HUMAN BRAIN MAPPING 2025年第4期46卷 e70184页

作者： Zhao, Hui Qi, Wang Xu, Jiahua Yao, Yaxin Lyu, Jianing Yang, Jiaxin Qin, Shaozheng Beijing Normal Univ McGovern Inst Brain Res State Key Lab Cognit Neurosci & Learning & IDG Beijing Peoples R China Peking Univ Beijing HuiLongGuan Hosp Psychiat Res Ctr Huilongguan Clin Med Sch Beijing Peoples R China Beijing Normal Univ Beijing Key Lab Brain Imaging & Connect Beijing Peoples R China

Focusing on the distance between magnitudes as the starting point to investigate the mechanism of relation detection and its contribution to mathematical thinking, this study explores the precision of neural representations of numerical distance and their impact on children's arithmetic performance. By employing neural decoding techniques and representational similarity analysis, the present study investigates how accurately the brain represents numerical distances and how this precision relates to arithmetic skills. Twenty-nine school-aged children participated, completing a dot number comparison task during fMRI scanning and an arithmetic fluency test. Results indicated that neural activation patterns in the intra-parietal sulcus decoded the distance between the presented pair of dots, and higher precision in neural distance representation correlates with better arithmetic performance. These findings suggest that the accuracy of neural decoding can serve as an index of neural representation precision and that the ability to precisely encode numerical distances in the brain is a key factor in mathematical abilities. This provides new insights into the neural basis of mathematical cognition and learning.

关键词： arithmetic skill intra-parietal sulcus neural decoding neural representation precision of distance representational similarity analysis

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decoding thoughts, encoding ethics: A narrative review of the BCI-AI revolution

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BRAIN RESEARCH 2025年 1850卷 149423-149423页

作者： Rudroff, Thorsten Univ Turku Turku PET Ctr Turku Finland Turku Univ Hosp Turku Finland

Objectives: This narrative review aims to analyze mechanisms underlying Brain-Computer Interface (BCI) and Artificial Intelligence (AI) integration, evaluate recent advances in signal acquisition and processing techniques, and assess AI-enhanced neural decoding strategies. The review identifies critical research gaps and examines emerging solutions across multiple domains of BCI-AI integration. Methods: A narrative review was conducted using major biomedical and scientific databases including PubMed, Web of Science, IEEE Xplore, and Scopus (2014-2024). Literature was analyzed to identify key developments in BCI-AI integration, with particular emphasis on recent advances (2019-2024). The review process involved thematic analysis of selected publications focusing on practical applications, technical innovations, and emerging challenges. Results: Recent advances demonstrate significant improvements in BCI-AI systems: 1) High-density electrode arrays achieve spatial resolution up to 5 mm, with stable recordings over 15 months;2) Deep learning decoders show 40 % improvement in information transfer rates compared to traditional methods;3) Adaptive algorithms maintain >90 % success rates in motor control tasks over 200-day periods without recalibration;4) Novel closed- loop optimization frameworks reduce user training time by 55 % while improving accuracy. Latest developments in flexible neural interfaces and self-supervised learning approaches show promise in addressing long-term stability and cross-user generalization challenges. Conclusions: BCI-AI integration shows remarkable progress in improving signal quality, decoding accuracy, and user adaptation. While challenges remain in long-term stability and user training, advances in adaptive algorithms and feedback mechanisms demonstrate the technology's growing viability for clinical applications. Recent innovations in electrode technology, AI architectures, and closed-loop systems, combined with emerging standardization

关键词： Brain-computer interfaces Artificial intelligence Signal processing neural decoding Cognition Feedback mechanisms

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An optogenetics- and imaging-assisted simultaneous multiple patch-clamp recording system for decoding complex neural circuits

An optogenetics- and imaging-assisted simultaneous multiple ...

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作者： Guangfu Wang Daniel R Wyskiel Weiguo Yang Yiqing Wang Lana C Milbern Txomin Lalanne Xiaolong Jiang Ying Shen Qian-Quan Sun J Julius Zhu

Deciphering neuronal circuitry is central to understanding brain function and dysfunction, yet it remains a daunting task. To facilitate the dissection of neuronal circuits, a process requiring functional analysis of synaptic connections and morphological identification of interconnected neurons, we present here a method for stable simultaneous octuple patch-clamp recordings. This method allows physiological analysis of synaptic interconnections among 4–8 simultaneously recorded neurons and/or 10–30 sequentially recorded neurons, and it allows anatomical identification of >85% of recorded interneurons and >99% of recorded principal neurons. We describe how to apply the method to rodent tissue slices; however, it can be used on other model organisms. We also describe the latest refinements and optimizations of mechanics, electronics, optics and software programs that are central to the realization of a combined single- and two-photon microscopy–based, optogenetics- and imaging-assisted, stable, simultaneous quadruple–viguple patch-clamp recording system. Setting up the system, from the beginning of instrument assembly and software installation to full operation, can be completed in 3–4 d. less

关键词： Patch Clamp Technique Dissection Microscopy Decapitation Immunostaining Neuronal physiology Synaptic transmission neural decoding

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Control of Redundant Kinematic Degrees of Freedom in a Closed-Loop Brain-Machine Interface

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IEEE TRANSACTIONS ON neural SYSTEMS AND REHABILITATION ENGINEERING 2017年第6期25卷 750-760页

作者： Moorman, Helene G. Gowda, Suraj Carmena, Jose M. Univ Calif Berkeley Inst Neurosci Berkeley CA 94720 USA Univ Calif Berkeley Dept Elect Engn & Comp Sci Berkeley CA 94720 USA Univ Calif Berkeley Helen Wills Neurosci Inst Berkeley CA 94720 USA

Brain-machine interface (BMI) systems use signals acquired from the brain to directly control the movement of an actuator, such as a computer cursor or a robotic arm, with the goal of restoring motor function lost due to injury or disease of the nervous system. In BMIs with kinematically redundant actuators, the combination of the task goals and the system under neural control can allow for many equally optimal task solutions. The extent to which kinematically redundant degrees of freedom (DOFs) in a BMI system may be under direct neural control is unknown. To address this question, a Kalman filter was used to decode single-and multi-unit cortical neural activity of two macaque monkeys into the joint velocities of a virtual four-link kinematic chain. Subjects completed movements of the chain's endpoint to instructed target locations within a two-dimensional plane. This system was kinematically redundant for an endpoint movement task, as four DOFs were used to manipulate the 2-D endpoint position. Both subjects successfully performed the task and improved with practice by producing faster endpoint velocity control signals. Kinematic redundancy allowed null movements whereby the individual links of the chain could move in away that cancels out and does not result in endpoint movement. As the subjects became more proficient at controlling the chain, the amount of null movement also increased. Task performance suffered when the links of the kinematic chain were hidden and only the endpoint was visible. Furthermore, all four DOFs of the joint-velocity control space exhibited task-relevant modulation. The relative usage of each DOF depended on the configuration of the chain, and trials in which the less-prominent DOFs were utilized also had better task performance. Overall, these results indicate that the subjects incorporated the redundant components of the control space into their control strategy. Future BMI systems with kinematic redundancy, such as exoskeletal system

关键词： Brain-machine interface (BMI) neural decoding non-human primate redundant control

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