scholarly journals Tracking neural activity from the same cells during the entire adult life of mice

2021 ◽  
Author(s):  
Siyuan Zhao ◽  
Xin Tang ◽  
Sebastian Partarrieu ◽  
Shiqi Guo ◽  
Ren Liu ◽  
...  
Keyword(s):  
Single Neuron ◽  
Adult Life ◽  
Brain Regions ◽  
Machine Learning Algorithms ◽  
Mesh Structure ◽  
Noise Rejection ◽  
The Neural Network ◽  
Neuroscience Research ◽  
Open Mesh ◽  
Unit Action

Recording the activity of the same neurons over the adult life of an animal is important to neuroscience research and biomedical applications. Current implantable devices cannot provide stable recording on this time scale. Here, we introduce a method to precisely implant nanoelectronics with an open, unfolded mesh structure across multiple brain regions in the mouse. The open mesh structure forms a stable interwoven structure with the neural network, preventing probe drifting and showing no immune response and neuron loss during the yearlong implantation. Using the implanted nanoelectronics, we can track single-unit action potentials from the same neurons over the entire adult life of mice. Leveraging the stable recordings, we build machine learning algorithms that enable automated spike sorting, noise rejection, stability validation, and generate pseudotime analysis, revealing aging-associated evolution of the single-neuron activities.

scholarly journals A novel framework for designing a multi-DoF prosthetic wrist control using machine learning

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Chinmay P. Swami ◽  
Nicholas Lenhard ◽  
Jiyeon Kang
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Upper Limb ◽  
Daily Living ◽  
Machine Learning Algorithms ◽  
Data Sets ◽  
Random Forest Regression ◽  
Prosthetic Devices ◽  
Upper Limb Function ◽  
The Neural Network

AbstractProsthetic arms can significantly increase the upper limb function of individuals with upper limb loss, however despite the development of various multi-DoF prosthetic arms the rate of prosthesis abandonment is still high. One of the major challenges is to design a multi-DoF controller that has high precision, robustness, and intuitiveness for daily use. The present study demonstrates a novel framework for developing a controller leveraging machine learning algorithms and movement synergies to implement natural control of a 2-DoF prosthetic wrist for activities of daily living (ADL). The data was collected during ADL tasks of ten individuals with a wrist brace emulating the absence of wrist function. Using this data, the neural network classifies the movement and then random forest regression computes the desired velocity of the prosthetic wrist. The models were trained/tested with ADLs where their robustness was tested using cross-validation and holdout data sets. The proposed framework demonstrated high accuracy (F-1 score of 99% for the classifier and Pearson’s correlation of 0.98 for the regression). Additionally, the interpretable nature of random forest regression was used to verify the targeted movement synergies. The present work provides a novel and effective framework to develop an intuitive control for multi-DoF prosthetic devices.

scholarly journals A Social Neuroscience Approach to Interpersonal Interaction in the Context of Disruption and Disorganization of Attachment (NAMDA)

2020 ◽  
Author(s):  
Lars White ◽  
Charlotte Catharina Schulz ◽  
Margerete Schött ◽  
Melanie Kungl ◽  
Jan Keil ◽  
...  
Keyword(s):  
Early Childhood ◽  
Emotion Regulation ◽  
Mental Representations ◽  
Brain Regions ◽  
Social Neuroscience ◽  
Neural Structure ◽  
Internal Working ◽  
Neuroscience Research ◽  
Attachment Disruption

Humans are strongly dependent upon social resources for allostasis and emotion regulation. This applies especially to early childhood because humans – as an altricial species – have a prolonged period of dependency on support and input from caregivers who typically act as sources of co-regulation. Accordingly, attachment theory proposes that the history and quality of early interactions with primary caregivers shape children’s internal working models of attachment. In turn, these attachment models guide behavior, initially with the set goal of maintaining proximity to caregivers, but eventually paving the way to more generalized mental representations of self and others. Mounting evidence in nonclinical populations suggests that these mental representations coincide with differential patterns of neural structure, function, and connectivity in a range of brain regions previously associated with emotional and cognitive capacities. What is currently lacking, however, is an evidence-based account of how early adverse attachment-related experiences and/or the emergence of attachment disorganization impact the developing brain. While work on early childhood adversities offers important insights, we propose that how these events become biologically embedded crucially hinges on the context of the child-caregiver attachment relationships in which the events take place. Our selective review distinguishes between direct social neuroscience research on disorganized attachment and indirect maltreatment-related research, converging on aberrant functioning in neurobiological systems subserving aversion, approach, emotion regulation, and mental state processing in the wake of severe attachment disruption. To account for heterogeneity of findings, we propose two distinct neurobiological phenotypes characterized by hyper- and hypo-arousal primarily deriving from the caregiver serving either as a threatening or as an insufficient source of co-regulation, respectively.

A comparison of deep machine learning and Monte Carlo methods for facies classification from seismic data

Geophysics ◽  
2020 ◽  
Vol 85 (4) ◽  
pp. WA41-WA52 ◽  
Author(s):  
Dario Grana ◽  
Leonardo Azevedo ◽  
Mingliang Liu
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Monte Carlo ◽  
Monte Carlo Methods ◽  
Seismic Data ◽  
Transition Probabilities ◽  
Machine Learning Algorithms ◽  
Data Set ◽  
Facies Classification ◽  
The Neural Network

Among the large variety of mathematical and computational methods for estimating reservoir properties such as facies and petrophysical variables from geophysical data, deep machine-learning algorithms have gained significant popularity for their ability to obtain accurate solutions for geophysical inverse problems in which the physical models are partially unknown. Solutions of classification and inversion problems are generally not unique, and uncertainty quantification studies are required to quantify the uncertainty in the model predictions and determine the precision of the results. Probabilistic methods, such as Monte Carlo approaches, provide a reliable approach for capturing the variability of the set of possible models that match the measured data. Here, we focused on the classification of facies from seismic data and benchmarked the performance of three different algorithms: recurrent neural network, Monte Carlo acceptance/rejection sampling, and Markov chain Monte Carlo. We tested and validated these approaches at the well locations by comparing classification predictions to the reference facies profile. The accuracy of the classification results is defined as the mismatch between the predictions and the log facies profile. Our study found that when the training data set of the neural network is large enough and the prior information about the transition probabilities of the facies in the Monte Carlo approach is not informative, machine-learning methods lead to more accurate solutions; however, the uncertainty of the solution might be underestimated. When some prior knowledge of the facies model is available, for example, from nearby wells, Monte Carlo methods provide solutions with similar accuracy to the neural network and allow a more robust quantification of the uncertainty, of the solution.

scholarly journals Neural network modeling and single-neuron proportional–integral–derivative control for hysteresis in piezoelectric actuators

2019 ◽  
Vol 52 (9-10) ◽  
pp. 1362-1370 ◽  
Author(s):  
Yuen Liang ◽  
Suan Xu ◽  
Kaixing Hong ◽  
Guirong Wang ◽  
Tao Zeng
Keyword(s):  
Neural Network ◽  
Single Neuron ◽  
Piezoelectric Actuators ◽  
Neural Network Modeling ◽  
Proportional Integral Derivative ◽  
Polynomial Fitting ◽  
Proportional Integral ◽  
The Neural Network ◽  
Fitting Method ◽  
One To One

A new polynomial fitting model based on a neural network is presented to characterize the hysteresis in piezoelectric actuators. As hysteresis is multi-valued mapping, and traditional neural networks can only solve one-to-one mapping, a hysteresis mathematical model is proposed to expand the input of the neural network by converting the multi-valued into one-to-one mapping. Experiments were performed under designed excitation with different driven voltage amplitudes to obtain the parameters of the model using the polynomial fitting method. The simulation results were in good accordance with the measured data and demonstrate the precision with which the model can predict the hysteresis. Based on the proposed model, a single-neuron adaptive proportional–integral–derivative controller combined with a feedforward loop is designed to correct the errors induced by the hysteresis in the piezoelectric actuator. The results demonstrate superior tracking performance, which validates the practicability and effectiveness of the presented approach.

Optimal Sequential Detection of Stimuli from Multiunit Recordings Taken in Densely Populated Brain Regions

2012 ◽  
Vol 24 (4) ◽  
pp. 895-938 ◽  
Author(s):  
Nir Nossenson ◽  
Hagit Messer
Keyword(s):  
Order Statistics ◽  
Single Neuron ◽  
Brain Regions ◽  
Second Order ◽  
Detection Scheme ◽  
Second Order Statistics ◽  
Optimal Detector ◽  
Electrode Voltage ◽  
The Subject ◽  
High Pass

We address the problem of detecting the presence of a recurring stimulus by monitoring the voltage on a multiunit electrode located in a brain region densely populated by stimulus reactive neurons. Published experimental results suggest that under these conditions, when a stimulus is present, the measurements are gaussian with typical second-order statistics. In this letter we systematically derive a generic, optimal detector for the presence of a stimulus in these conditions and describe its implementation. The optimality of the proposed detector is in the sense that it maximizes the life span (or time to injury) of the subject. In addition, we construct a model for the acquired multiunit signal drawing on basic assumptions regarding the nature of a single neuron, which explains the second-order statistics of the raw electrode voltage measurements that are high-pass-filtered above 300 Hz. The operation of the optimal detector and that of a simpler suboptimal detection scheme is demonstrated by simulations and on real electrophysiological data.

Machine Learning in Gifted Education: A Demonstration Using Neural Networks

2019 ◽  
Vol 63 (4) ◽  
pp. 243-252 ◽  
Author(s):  
Jaret Hodges ◽  
Soumya Mohan
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Supervised Learning ◽  
Language Processing ◽  
Gifted Education ◽  
Learning Algorithms ◽  
Simulated Data ◽  
Machine Learning Algorithms ◽  
Automated Driving ◽  
The Neural Network

Machine learning algorithms are used in language processing, automated driving, and for prediction. Though the theory of machine learning has existed since the 1950s, it was not until the advent of advanced computing that their potential has begun to be realized. Gifted education is a field where machine learning has yet to be utilized, even though one of the underlying problems of gifted education is classification, which is an area where learning algorithms have become exceptionally accurate. We provide a brief overview of machine learning with a focus on neural networks and supervised learning, followed by a demonstration using simulated data and neural networks for classification issues with a practical explanation of the mechanics of the neural network and associated R code. Implications for gifted education are then discussed. Finally, the limitations of supervised learning are discussed. Code used in this article can be found at https://osf.io/4pa3b/

scholarly journals Highly scalable multichannel mesh electronics for stable chronic brain electrophysiology

2017 ◽  
Vol 114 (47) ◽  
pp. E10046-E10055 ◽  
Author(s):  
Tian-Ming Fu ◽  
Guosong Hong ◽  
Robert D. Viveros ◽  
Tao Zhou ◽  
Charles M. Lieber
Keyword(s):  
Large Scale ◽  
Single Neuron ◽  
Neurological Diseases ◽  
Field Potential ◽  
Brain Regions ◽  
Long Term Stability ◽  
High Density Recording ◽  
Local Circuits ◽  
Key Aspects

Implantable electrical probes have led to advances in neuroscience, brain−machine interfaces, and treatment of neurological diseases, yet they remain limited in several key aspects. Ideally, an electrical probe should be capable of recording from large numbers of neurons across multiple local circuits and, importantly, allow stable tracking of the evolution of these neurons over the entire course of study. Silicon probes based on microfabrication can yield large-scale, high-density recording but face challenges of chronic gliosis and instability due to mechanical and structural mismatch with the brain. Ultraflexible mesh electronics, on the other hand, have demonstrated negligible chronic immune response and stable long-term brain monitoring at single-neuron level, although, to date, it has been limited to 16 channels. Here, we present a scalable scheme for highly multiplexed mesh electronics probes to bridge the gap between scalability and flexibility, where 32 to 128 channels per probe were implemented while the crucial brain-like structure and mechanics were maintained. Combining this mesh design with multisite injection, we demonstrate stable 128-channel local field potential and single-unit recordings from multiple brain regions in awake restrained mice over 4 mo. In addition, the newly integrated mesh is used to validate stable chronic recordings in freely behaving mice. This scalable scheme for mesh electronics together with demonstrated long-term stability represent important progress toward the realization of ideal implantable electrical probes allowing for mapping and tracking single-neuron level circuit changes associated with learning, aging, and neurodegenerative diseases.

scholarly journals High-throughput mapping of single-cell molecular and projection architecture of neurons by retrograde barcoded labelling

2021 ◽  
Author(s):  
Peibo Xu ◽  
Jian Peng ◽  
Tingli Yuan ◽  
Zhaoqin Chen ◽  
Ziyan Wu ◽  
...  
Keyword(s):  
Single Cell ◽  
Single Neuron ◽  
Neural Circuit ◽  
Brain Regions ◽  
Mammalian Brain ◽  
Projection Neurons ◽  
Proof Of Concept ◽  
Sequencing Technology ◽  
Modal Data ◽  
Projection Pattern

Deciphering mesoscopic connectivity of the mammalian brain is a pivotal step in neuroscience. Most imaging-based conventional neuroanatomical tracing methods identify area-to-area or sparse single neuronal labeling information. Although recently developed barcode-based connectomics has been able to map a large number of single-neuron projections efficiently, there is a missing link in single-cell connectome and transcriptome. Here, combining single-cell RNA sequencing technology, we established a retro-AAV barcode-based multiplexed tracing method called MEGRE-seq (Multiplexed projEction neuRons retroGrade barcodE), which can resolve projectome and transcriptome of source neurons simultaneously. Using the ventromedial prefrontal cortex (vmPFC) as a proof-of-concept neocortical region, we investigated projection patterns of its excitatory neurons targeting five canonical brain regions, as well as corresponding transcriptional profiles. Dedicated, bifurcated or collateral projection patterns were inferred by digital projectome. In combination with simultaneously recovered transcriptome, we find that certain projection pattern has a preferential layer or neuron subtype bias. Further, we fitted single-neuron two-modal data into a machine learning-based model and delineated gene importance by each projection target. In summary, we anticipate that the new multiplexed digital connectome technique is potential to understand the organizing principle of the neural circuit by linking projectome and transcriptome.

scholarly journals Effective Methodology for Co-Referential Aspect Based Sentiment Analysis of Tourist Reviews

2020 ◽  
pp. 523-529
Author(s):  
Kamlesh A. Waghmare ◽  
Sheetal K. Bhala
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Sentiment Analysis ◽  
High Accuracy ◽  
Machine Learning Algorithms ◽  
Data Sets ◽  
Knowledge Processing ◽  
The Neural Network ◽  
The World ◽  
Sentiment Score

Tourist reviews are the source of data that is going to be used for the travelers around the world to find the hotels for their stay according to their comfort. In this the hotels are ranked over the parameters or aspects considered keeping travelers in mind. This computation of data sets is done with the help of the machine learning algorithms and the neural network. The knowledge processing done over the reviews generates the sentiment score for each hotel with respect to the aspects defined. Here, the explicit , implicit and co-referential aspects are identified by suppressing the noise. This paper proposes the method that can be best used for the detection of the sentiments with the high accuracy.

scholarly journals Performance Improvement in Steam Turbine in Thermal Power Plants Using Artificial Neural Network

2021 ◽  
Vol 54 (6) ◽  
pp. 891-895
Author(s):  
Fawaz S. Abdullah ◽  
Ali N. Hamoodi ◽  
Rasha A. Mohammed
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Artificial Neural Network ◽  
Power Plants ◽  
Thermal Power ◽  
Machine Learning Algorithms ◽  
Circuit Board ◽  
Electric Circuits ◽  
The Neural Network ◽  
Artificial Neural

Artificial intelligence has proven its effectiveness in many industrial fields to enhance the existing functionality. Artificial intelligence and machine learning algorithms integrated with turbines can be useful in controlling important variables such as pressure, temperature, speed, and humidity. In this research, the Simulink library from MATLAB is used to build an artificial neural network. The NARMA L2 neural controller is used to generate data and for training networks. To obtain the result and compare it with the real-time power plant, data is collected. The input variables provided to the neural network have a large effect on the hidden layer and the output of the neural network. The circuit board used in this research has a DC bridge, a transformer and voltage regulators. The result comparison shows that the integration of artificial neural networks and electric circuits shows enhanced performance with high accuracy of prediction. It was observed that the ANN integration system and electric circuit design have a result deviation of less than 1%. This shows that the integration of ANN improves the performance of turbines.

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