17,381 research outputs found
A Mixed Data-Based Deep Neural Network to Estimate Leaf Area Index in Wheat Breeding Trials
Remote and non-destructive estimation of leaf area index (LAI) has been a challenge in
the last few decades as the direct and indirect methods available are laborious and
time-consuming. The recent emergence of high-throughput plant phenotyping platforms has
increased the need to develop new phenotyping tools for better decision-making by breeders. In
this paper, a novel model based on artificial intelligence algorithms and nadir-view red green blue
(RGB) images taken from a terrestrial high throughput phenotyping platform is presented. The
model mixes numerical data collected in a wheat breeding field and visual features extracted from
the images to make rapid and accurate LAI estimations. Model-based LAI estimations were
validated against LAI measurements determined non-destructively using an allometric
relationship obtained in this study. The model performance was also compared with LAI estimates
obtained by other classical indirect methods based on bottom-up hemispherical images and gaps
fraction theory. Model-based LAI estimations were highly correlated with ground-truth LAI. The
model performance was slightly better than that of the hemispherical image-based method, which
tended to underestimate LAI. These results show the great potential of the developed model for
near real-time LAI estimation, which can be further improved in the future by increasing the
dataset used to train the model
Training deep neural density estimators to identify mechanistic models of neural dynamics
Mechanistic modeling in neuroscience aims to explain observed phenomena in terms of underlying causes. However, determining which model parameters agree with complex and stochastic neural data presents a significant challenge. We address this challenge with a machine learning tool which uses deep neural density estimators-- trained using model simulations-- to carry out Bayesian inference and retrieve the full space of parameters compatible with raw data or selected data features. Our method is scalable in parameters and data features, and can rapidly analyze new data after initial training. We demonstrate the power and flexibility of our approach on receptive fields, ion channels, and Hodgkin-Huxley models. We also characterize the space of circuit configurations giving rise to rhythmic activity in the crustacean stomatogastric ganglion, and use these results to derive hypotheses for underlying compensation mechanisms. Our approach will help close the gap between data-driven and theory-driven models of neural dynamics
Structure Learning in Coupled Dynamical Systems and Dynamic Causal Modelling
Identifying a coupled dynamical system out of many plausible candidates, each
of which could serve as the underlying generator of some observed measurements,
is a profoundly ill posed problem that commonly arises when modelling real
world phenomena. In this review, we detail a set of statistical procedures for
inferring the structure of nonlinear coupled dynamical systems (structure
learning), which has proved useful in neuroscience research. A key focus here
is the comparison of competing models of (ie, hypotheses about) network
architectures and implicit coupling functions in terms of their Bayesian model
evidence. These methods are collectively referred to as dynamical casual
modelling (DCM). We focus on a relatively new approach that is proving
remarkably useful; namely, Bayesian model reduction (BMR), which enables rapid
evaluation and comparison of models that differ in their network architecture.
We illustrate the usefulness of these techniques through modelling
neurovascular coupling (cellular pathways linking neuronal and vascular
systems), whose function is an active focus of research in neurobiology and the
imaging of coupled neuronal systems
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