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      Inference in the age of big data: Future perspectives on neuroscience

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      NeuroImage

      Elsevier BV

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          Deep Learning in Neural Networks: An Overview

            (2014)
          In recent years, deep artificial neural networks (including recurrent ones) have won numerous contests in pattern recognition and machine learning. This historical survey compactly summarises relevant work, much of it from the previous millennium. Shallow and deep learners are distinguished by the depth of their credit assignment paths, which are chains of possibly learnable, causal links between actions and effects. I review deep supervised learning (also recapitulating the history of backpropagation), unsupervised learning, reinforcement learning & evolutionary computation, and indirect search for short programs encoding deep and large networks.
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            The ASA's Statement onp-Values: Context, Process, and Purpose

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              Noise in the nervous system.

              Noise--random disturbances of signals--poses a fundamental problem for information processing and affects all aspects of nervous-system function. However, the nature, amount and impact of noise in the nervous system have only recently been addressed in a quantitative manner. Experimental and computational methods have shown that multiple noise sources contribute to cellular and behavioural trial-to-trial variability. We review the sources of noise in the nervous system, from the molecular to the behavioural level, and show how noise contributes to trial-to-trial variability. We highlight how noise affects neuronal networks and the principles the nervous system applies to counter detrimental effects of noise, and briefly discuss noise's potential benefits.
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                Author and article information

                Journal
                NeuroImage
                NeuroImage
                Elsevier BV
                10538119
                April 2017
                April 2017
                :
                :
                Article
                10.1016/j.neuroimage.2017.04.061
                © 2017

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