scholarly journals Behavioural analysis of single-cell aneural ciliate,Stentor roeselii, using machine learning approaches

2019 ◽  
Author(s):  
Kiều Mi Trịnh ◽  
Matthew T. Wayland ◽  
Sudhakaran Prabakaran
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Neural Circuits ◽  
Neural Systems ◽  
Learning Approaches ◽  
Learning Behaviour ◽  
Decision Making Processes ◽  
Feed Forward Neural Networks ◽  
Unicellular Organisms ◽  
Fully Functioning

AbstractThere is still a significant gap between our understanding of neural circuits and the behaviours they compute – i.e. the computations performed by these neural networks (Carandini 2012). Learning, behaviour, and memory formation, what used to only be associated with animals with neural systems, have been observed in many unicellular aneural species, namely Physarum, Paramecium, and Stentor (Tang & Marshall 2018). As these are fully functioning organisms, yet being unicellular, there is a much better chance to elucidate the detailed mechanisms underlying these learning processes in these organisms without the complications of highly interconnected neural circuits. An intriguing learning behaviour observed inStentor roeselii(Jennings 1902) when stimulated with carmine has left scientists puzzled for more than a century. So far, none of the existing learning paradigm can fully encapsulate this particular series of five characteristic avoidant reactions. Although we were able to observe all responses described in literature and in a previous study (Dexter et al. 2019, manuscript in preparation), they do not conform to any particular learning model. We then investigated whether models based on machine learning approaches, including decision tree, random forest, and feed-forward neural networks could infer and predict the behavior ofS. roeselii. Our results showed that an artificial neural network with multiple ‘computational’ neurons is inefficient at modelling the single-celled ciliate’s avoidant reactions. This has highlighted the complexity of behaviours in aneural organisms. Additionally, this report will also discuss the significance of elucidating molecular details underlying learning and decision-making processes in these unicellular organisms, which could offer valuable insights that are applicable to higher animals.

scholarly journals Behavioural analysis of single-cell aneural ciliate, Stentor roeseli, using machine learning approaches

2019 ◽  
Vol 16 (161) ◽  
pp. 20190410
Author(s):  
Mi Kieu Trinh ◽  
Matthew T. Wayland ◽  
Sudhakaran Prabakaran
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Decision Making ◽  
Neural Circuits ◽  
Learning Approaches ◽  
Learning Behaviour ◽  
Link Type ◽  
Decision Making Processes ◽  
Artificial Neural ◽  
Avoidance Reactions

There is still a significant gap between our understanding of neural circuits and the behaviours they compute—i.e. the computations performed by these neural networks (Carandini 2012 Nat. Neurosci. 15 , 507–509. ( doi:10.1038/nn.3043 )). Cellular decision-making processes, learning, behaviour and memory formation—all that have been only associated with animals with neural systems—have also been observed in many unicellular aneural organisms, namely Physarum , Paramecium and Stentor (Tang & Marshall2018 Curr. Biol. 28 , R1180–R1184. ( doi:10.1016/j.cub.2018.09.015 )). As these are fully functioning organisms, yet being unicellular, there is a much better chance to elucidate the detailed mechanisms underlying these learning processes in these organisms without the complications of highly interconnected neural circuits. An intriguing learning behaviour observed in Stentor roeseli (Jennings 1902 Am. J. Physiol. Legacy Content 8 , 23–60. ( doi:10.1152/ajplegacy.1902.8.1.23 )) when stimulated with carmine has left scientists puzzled for more than a century. So far, none of the existing learning paradigm can fully encapsulate this particular series of five characteristic avoidance reactions. Although we were able to observe all responses described in the literature and in a previous study (Dexter et al . 2019), they do not conform to any particular learning model. We then investigated whether models inferred from machine learning approaches, including decision tree, random forest and feed-forward artificial neural networks could infer and predict the behaviour of S. roeseli . Our results showed that an artificial neural network with multiple ‘computational’ neurons is inefficient at modelling the single-celled ciliate's avoidance reactions. This has highlighted the complexity of behaviours in aneural organisms. Additionally, this report will also discuss the significance of elucidating molecular details underlying learning and decision-making processes in these unicellular organisms, which could offer valuable insights that are applicable to higher animals.

Computerised Decision Support for Women’s Health Informatics

2012 ◽  
pp. 1404-1416 ◽  
Author(s):  
David Parry
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Decision Making ◽  
Decision Support ◽  
Expert Systems ◽  
Decision Analysis ◽  
Health Informatics ◽  
Learning Approaches ◽  
Learning From Data ◽  
Computerized Records

Decision analysis techniques attempt to utilize mathematical data about outcomes and preferences to help people make optimal decisions. The increasing uses of computerized records and powerful computers have made these techniques much more accessible and usable. The partnership between women and clinicians can be enhanced by sharing information, knowledge, and the decision making process in this way. Other techniques for assisting with decision making, such as learning from data via neural networks or other machine learning approaches may offer increased value. Rules learned from such approaches may allow the development of expert systems that actually take over some of the decision making role, although such systems are not yet in widespread use.

Computerised Decision Support for Women's Health Informatics

2009 ◽  
pp. 302-314
Author(s):  
David Parry
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Decision Making ◽  
Decision Support ◽  
Expert Systems ◽  
Decision Analysis ◽  
Health Informatics ◽  
Learning Approaches ◽  
Learning From Data ◽  
Computerized Records

Decision analysis techniques attempt to utilize mathematical data about outcomes and preferences to help people make optimal decisions. The increasing uses of computerized records and powerful computers have made these techniques much more accessible and usable. The partnership between women and clinicians can be enhanced by sharing information, knowledge, and the decision making process in this way. Other techniques for assisting with decision making, such as learning from data via neural networks or other machine learning approaches may offer increased value. Rules learned from such approaches may allow the development of expert systems that actually take over some of the decision making role, although such systems are not yet in widespread use.

scholarly journals Harnessing Deep Neural Networks to Solve Inverse Problems in Quantum Dynamics: Machine-Learned Predictions of Time-Dependent Optimal Control Fields

2020 ◽  
Author(s):  
Xian Wang ◽  
Anshuman Kumar ◽  
Christian Shelton ◽  
Bryan Wong
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Neural Networks ◽  
Optimal Control ◽  
Quantum Dynamics ◽  
Deep Neural Networks ◽  
Time Dependent ◽  
Learning Approaches ◽  
Quantum Dynamical ◽  
Quantum Dynamical Systems

Inverse problems continue to garner immense interest in the physical sciences, particularly in the context of controlling desired phenomena in non-equilibrium systems. In this work, we utilize a series of deep neural networks for predicting time-dependent optimal control fields, <i>E(t)</i>, that enable desired electronic transitions in reduced-dimensional quantum dynamical systems. To solve this inverse problem, we investigated two independent machine learning approaches: (1) a feedforward neural network for predicting the frequency and amplitude content of the power spectrum in the frequency domain (i.e., the Fourier transform of <i>E(t)</i>), and (2) a cross-correlation neural network approach for directly predicting <i>E(t)</i> in the time domain. Both of these machine learning methods give complementary approaches for probing the underlying quantum dynamics and also exhibit impressive performance in accurately predicting both the frequency and strength of the optimal control field. We provide detailed architectures and hyperparameters for these deep neural networks as well as performance metrics for each of our machine-learned models. From these results, we show that machine learning approaches, particularly deep neural networks, can be employed as a cost-effective statistical approach for designing electromagnetic fields to enable desired transitions in these quantum dynamical systems.

scholarly journals Diagnosis of Brain Diseases using Neural Networks

2019 ◽  
Vol 9 (2S) ◽  
pp. 402-407
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Deep Learning ◽  
Neurological Diseases ◽  
Brain Diseases ◽  
Learning Approaches ◽  
Learning Practices ◽  
Computer Aided Analysis ◽  
Computer Aided ◽  
The Brain

Intensification in the occurrence of brain diseases and the need for the initial diagnosis for ailments like Tumor, Alzheimer’s, Epilepsy and Parkinson’s has riveted the attention of researchers. Machine learning practices, specifically deep learning, is considered as a beneficial diagnostic tool. Deep learning approaches to neuroimaging will assist computer-aided analysis of neurological diseases. Feature extraction of neuroimages carried out using Artificial Neural Networks leads to better diagnoses. In this study, all the brain diseases are revisited to consolidate the methodologies carried out by various authors in the literature.

scholarly journals Generalizable Machine Learning in Neuroscience Using Graph Neural Networks

2021 ◽  
Vol 4 ◽  
Author(s):  
Paul Y. Wang ◽  
Sandalika Sapra ◽  
Vivek Kurien George ◽  
Gabriel A. Silva
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Deep Learning ◽  
Neural Systems ◽  
Graph Structure ◽  
Imaging Data ◽  
C Elegans ◽  
State Classification ◽  
Graph Neural Networks ◽  
Emergent Behaviors

Although a number of studies have explored deep learning in neuroscience, the application of these algorithms to neural systems on a microscopic scale, i.e. parameters relevant to lower scales of organization, remains relatively novel. Motivated by advances in whole-brain imaging, we examined the performance of deep learning models on microscopic neural dynamics and resulting emergent behaviors using calcium imaging data from the nematode C. elegans. As one of the only species for which neuron-level dynamics can be recorded, C. elegans serves as the ideal organism for designing and testing models bridging recent advances in deep learning and established concepts in neuroscience. We show that neural networks perform remarkably well on both neuron-level dynamics prediction and behavioral state classification. In addition, we compared the performance of structure agnostic neural networks and graph neural networks to investigate if graph structure can be exploited as a favourable inductive bias. To perform this experiment, we designed a graph neural network which explicitly infers relations between neurons from neural activity and leverages the inferred graph structure during computations. In our experiments, we found that graph neural networks generally outperformed structure agnostic models and excel in generalization on unseen organisms, implying a potential path to generalizable machine learning in neuroscience.

scholarly journals Applying Deep Neural Networks and Ensemble Machine Learning Methods to Forecast Airborne Ambrosia Pollen

2019 ◽  
Vol 16 (11) ◽  
pp. 1992 ◽  
Author(s):  
Gebreab K. Zewdie ◽  
David J. Lary ◽  
Estelle Levetin ◽  
Gemechu F. Garuma
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Land Surface ◽  
Deep Neural Networks ◽  
Airborne Pollen ◽  
Training Data ◽  
Gradient Boosting ◽  
Learning Approaches ◽  
Ambrosia Pollen ◽  
Extreme Gradient Boosting

Allergies to airborne pollen are a significant issue affecting millions of Americans. Consequently, accurately predicting the daily concentration of airborne pollen is of significant public benefit in providing timely alerts. This study presents a method for the robust estimation of the concentration of airborne Ambrosia pollen using a suite of machine learning approaches including deep learning and ensemble learners. Each of these machine learning approaches utilize data from the European Centre for Medium-Range Weather Forecasts (ECMWF) atmospheric weather and land surface reanalysis. The machine learning approaches used for developing a suite of empirical models are deep neural networks, extreme gradient boosting, random forests and Bayesian ridge regression methods for developing our predictive model. The training data included twenty-four years of daily pollen concentration measurements together with ECMWF weather and land surface reanalysis data from 1987 to 2011 is used to develop the machine learning predictive models. The last six years of the dataset from 2012 to 2017 is used to independently test the performance of the machine learning models. The correlation coefficients between the estimated and actual pollen abundance for the independent validation datasets for the deep neural networks, random forest, extreme gradient boosting and Bayesian ridge were 0.82, 0.81, 0.81 and 0.75 respectively, showing that machine learning can be used to effectively forecast the concentrations of airborne pollen.

scholarly journals Can Artificial Intelligence Improve the Management of Pneumonia

2020 ◽  
Vol 9 (1) ◽  
pp. 248 ◽  
Author(s):  
Mariana Chumbita ◽  
Catia Cillóniz ◽  
Pedro Puerta-Alcalde ◽  
Estela Moreno-García ◽  
Gemma Sanjuan ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Neural Networks ◽  
Clinical Decision Making ◽  
Empirical Therapy ◽  
Clinical Decision ◽  
Mortality Prediction ◽  
Mathematical Functions ◽  
Computational Performance ◽  
Decision Making Processes

The use of artificial intelligence (AI) to support clinical medical decisions is a rather promising concept. There are two important factors that have driven these advances: the availability of data from electronic health records (EHR) and progress made in computational performance. These two concepts are interrelated with respect to complex mathematical functions such as machine learning (ML) or neural networks (NN). Indeed, some published articles have already demonstrated the potential of these approaches in medicine. When considering the diagnosis and management of pneumonia, the use of AI and chest X-ray (CXR) images primarily have been indicative of early diagnosis, prompt antimicrobial therapy, and ultimately, better prognosis. Coupled with this is the growing research involving empirical therapy and mortality prediction, too. Maximizing the power of NN, the majority of studies have reported high accuracy rates in their predictions. As AI can handle large amounts of data and execute mathematical functions such as machine learning and neural networks, AI can be revolutionary in supporting the clinical decision-making processes. In this review, we describe and discuss the most relevant studies of AI in pneumonia.

scholarly journals Harnessing Deep Neural Networks to Solve Inverse Problems in Quantum Dynamics: Machine-Learned Predictions of Time-Dependent Optimal Control Fields

2020 ◽  
Author(s):  
Xian Wang ◽  
Anshuman Kumar ◽  
Christian Shelton ◽  
Bryan Wong
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Neural Networks ◽  
Optimal Control ◽  
Quantum Dynamics ◽  
Deep Neural Networks ◽  
Time Dependent ◽  
Learning Approaches ◽  
Quantum Dynamical ◽  
Quantum Dynamical Systems

Inverse problems continue to garner immense interest in the physical sciences, particularly in the context of controlling desired phenomena in non-equilibrium systems. In this work, we utilize a series of deep neural networks for predicting time-dependent optimal control fields, <i>E(t)</i>, that enable desired electronic transitions in reduced-dimensional quantum dynamical systems. To solve this inverse problem, we investigated two independent machine learning approaches: (1) a feedforward neural network for predicting the frequency and amplitude content of the power spectrum in the frequency domain (i.e., the Fourier transform of <i>E(t)</i>), and (2) a cross-correlation neural network approach for directly predicting <i>E(t)</i> in the time domain. Both of these machine learning methods give complementary approaches for probing the underlying quantum dynamics and also exhibit impressive performance in accurately predicting both the frequency and strength of the optimal control field. We provide detailed architectures and hyperparameters for these deep neural networks as well as performance metrics for each of our machine-learned models. From these results, we show that machine learning approaches, particularly deep neural networks, can be employed as a cost-effective statistical approach for designing electromagnetic fields to enable desired transitions in these quantum dynamical systems.

scholarly journals Cross-situational word learning with multimodal neural networks

2021 ◽  
Author(s):  
Wai Keen Vong ◽  
Brenden M. Lake
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Neural Networks ◽  
Word Learning ◽  
Network Architecture ◽  
Question Answering ◽  
Cognitive Models ◽  
Learning Approaches ◽  
Neural Network Learning ◽  
Single Epoch

In order to learn the mappings from words to referents, children must integrate co-occurrence information across individually ambiguous pairs of scenes and utterances, a challenge known as cross-situational word learning. In machine learning, recent multimodal neural networks have been shown to learn meaningful visual-linguistic mappings from cross-situational data, as needed to solve problems such as image captioning and visual question answering. These networks are potentially appealing as cognitive models because they can learn from raw visual and linguistic stimuli, something previous cognitive models have not addressed. In this paper, we examine whether recent machine learning approaches can help explain various behavioral phenomena from the psychological literature on cross-situational word learning. We consider two variants of a multimodal neural network architecture, and look at seven different phenomena associated with cross-situational word learning, and word learning more generally. Our results show that these networks can learn word-referent mappings from a single epoch of training, matching the amount of training found in cross-situational word learning experiments. Additionally, these networks capture some, but not all of the phenomena we studied, with all of the failures related to reasoning via mutual exclusivity. These results provide insight into the kinds of phenomena that arise naturally from relatively generic neural network learning algorithms, and which word learning phenomena require additional inductive biases.

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