A guide to machine learning for biologists

Nature Reviews Molecular Cell Biology, Год журнала: 2021, Номер 23(1), С. 40 - 55

Опубликована: Сен. 13, 2021

Язык: Английский

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Computation Through Neural Population Dynamics

Annual Review of Neuroscience, Год журнала: 2020, Номер 43(1), С. 249 - 275

Опубликована: Июль 8, 2020

Significant experimental, computational, and theoretical work has identified rich structure within the coordinated activity of interconnected neural populations. An emerging challenge now is to uncover nature associated computations, how they are implemented, what role play in driving behavior. We term this computation through population dynamics. If successful, framework will reveal general motifs quantitatively describe dynamics implement computations necessary for goal-directed Here, we start with a mathematical primer on dynamical systems theory analytical tools apply perspective experimental data. Next, highlight some recent discoveries resulting from successful application systems. focus studies spanning motor control, timing, decision-making, working memory. Finally, briefly discuss promising lines investigation future directions framework.

Язык: Английский

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Long-term stability of cortical population dynamics underlying consistent behavior

Nature Neuroscience, Год журнала: 2020, Номер 23(2), С. 260 - 270

Опубликована: Янв. 6, 2020

Язык: Английский

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Artificial Neural Networks for Neuroscientists: A Primer

Neuron, Год журнала: 2020, Номер 107(6), С. 1048 - 1070

Опубликована: Сен. 1, 2020

Язык: Английский

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Unsupervised Discovery of Demixed, Low-Dimensional Neural Dynamics across Multiple Timescales through Tensor Component Analysis

Neuron, Год журнала: 2018, Номер 98(6), С. 1099 - 1115.e8

Опубликована: Июнь 1, 2018

Язык: Английский

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Towards the neural population doctrine

Current Opinion in Neurobiology, Год журнала: 2019, Номер 55, С. 103 - 111

Опубликована: Март 13, 2019

Язык: Английский

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Cortical pattern generation during dexterous movement is input-driven

Nature, Год журнала: 2019, Номер 577(7790), С. 386 - 391

Опубликована: Дек. 25, 2019

Язык: Английский

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Accurate Estimation of Neural Population Dynamics without Spike Sorting

Neuron, Год журнала: 2019, Номер 103(2), С. 292 - 308.e4

Опубликована: Июнь 3, 2019

Язык: Английский

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Bayesian Computation through Cortical Latent Dynamics

Neuron, Год журнала: 2019, Номер 103(5), С. 934 - 947.e5

Опубликована: Июль 15, 2019

Язык: Английский

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Training deep neural density estimators to identify mechanistic models of neural dynamics

eLife, Год журнала: 2020, Номер 9

Опубликована: Сен. 17, 2020

Mechanistic modeling in neuroscience aims to explain observed phenomena terms of underlying causes. However, determining which model parameters agree with complex and stochastic neural data presents a significant challenge. We address this challenge machine learning tool uses deep density estimators—trained using simulations—to carry out Bayesian inference retrieve the full space compatible raw or selected features. Our method is scalable features can rapidly analyze new after initial training. demonstrate power flexibility our approach on receptive fields, ion channels, Hodgkin–Huxley models. also characterize circuit configurations giving rise rhythmic activity crustacean stomatogastric ganglion, use these results derive hypotheses for compensation mechanisms. will help close gap between data-driven theory-driven models dynamics.

Язык: Английский

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