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      The Brain Ages Optimally to Model Its Environment: Evidence from Sensory Learning over the Adult Lifespan

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          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          The aging brain shows a progressive loss of neuropil, which is accompanied by subtle changes in neuronal plasticity, sensory learning and memory. Neurophysiologically, aging attenuates evoked responses—including the mismatch negativity (MMN). This is accompanied by a shift in cortical responsivity from sensory (posterior) regions to executive (anterior) regions, which has been interpreted as a compensatory response for cognitive decline. Theoretical neurobiology offers a simpler explanation for all of these effects—from a Bayesian perspective, as the brain is progressively optimized to model its world, its complexity will decrease. A corollary of this complexity reduction is an attenuation of Bayesian updating or sensory learning. Here we confirmed this hypothesis using magnetoencephalographic recordings of the mismatch negativity elicited in a large cohort of human subjects, in their third to ninth decade. Employing dynamic causal modeling to assay the synaptic mechanisms underlying these non-invasive recordings, we found a selective age-related attenuation of synaptic connectivity changes that underpin rapid sensory learning. In contrast, baseline synaptic connectivity strengths were consistently strong over the decades. Our findings suggest that the lifetime accrual of sensory experience optimizes functional brain architectures to enable efficient and generalizable predictions of the world.

          Author Summary

          While studies of aging are widely framed in terms of their demarcation of degenerative processes, the brain provides a unique opportunity to uncover the adaptive effects of getting older. Though intuitively reasonable, that life-experience and wisdom should reside somewhere in human cortex, these features have eluded neuroscientific explanation. The present study utilizes a “Bayesian Brain” framework to motivate an analysis of cortical circuit processing. From a Bayesian perspective, the brain represents a model of its environment and offers predictions about the world, while responding, through changing synaptic strengths to novel interactions and experiences. We hypothesized that these predictive and updating processes are modified as we age, representing an optimization of neuronal architecture. Using novel sensory stimuli we demonstrate that synaptic connections of older brains resist trial by trial learning to provide a robust model of their sensory environment. These older brains are capable of processing a wider range of sensory inputs – representing experienced generalists. We thus explain how, contrary to a singularly degenerative point-of-view, aging neurobiological effects may be understood, in sanguine terms, as adaptive and useful.

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          Most cited references 58

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          Hemispheric asymmetry reduction in older adults: the HAROLD model.

          A model of the effects of aging on brain activity during cognitive performance is introduced. The model is called HAROLD (hemispheric asymmetry reduction in older adults), and it states that, under similar circumstances, prefrontal activity during cognitive performances tends to be less lateralized in older adults than in younger adults. The model is supported by functional neuroimaging and other evidence in the domains of episodic memory, semantic memory, working memory, perception, and inhibitory control. Age-related hemispheric asymmetry reductions may have a compensatory function or they may reflect a dedifferentiation process. They may have a cognitive or neural origin, and they may reflect regional or network mechanisms. The HAROLD model is a cognitive neuroscience model that integrates ideas and findings from psychology and neuroscience of aging.
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            The free-energy principle: a rough guide to the brain?

             Karl Friston (2009)
            This article reviews a free-energy formulation that advances Helmholtz's agenda to find principles of brain function based on conservation laws and neuronal energy. It rests on advances in statistical physics, theoretical biology and machine learning to explain a remarkable range of facts about brain structure and function. We could have just scratched the surface of what this formulation offers; for example, it is becoming clear that the Bayesian brain is just one facet of the free-energy principle and that perception is an inevitable consequence of active exchange with the environment. Furthermore, one can see easily how constructs like memory, attention, value, reinforcement and salience might disclose their simple relationships within this framework.
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              The Helmholtz machine.

              Discovering the structure inherent in a set of patterns is a fundamental aim of statistical inference or learning. One fruitful approach is to build a parameterized stochastic generative model, independent draws from which are likely to produce the patterns. For all but the simplest generative models, each pattern can be generated in exponentially many ways. It is thus intractable to adjust the parameters to maximize the probability of the observed patterns. We describe a way of finessing this combinatorial explosion by maximizing an easily computed lower bound on the probability of the observations. Our method can be viewed as a form of hierarchical self-supervised learning that may relate to the function of bottom-up and top-down cortical processing pathways.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS Comput Biol
                PLoS Comput. Biol
                plos
                ploscomp
                PLoS Computational Biology
                Public Library of Science (San Francisco, USA )
                1553-734X
                1553-7358
                January 2014
                January 2014
                23 January 2014
                : 10
                : 1
                Affiliations
                [1 ]Virginia Tech Carilion Research Institute and Bradley Department of Electrical & Computer Engineering, Roanoke, Virginia, United States of America
                [2 ]Wellcome Trust Centre for Neuroimaging, Institute of Neurology, University College London, London, United Kingdom
                [3 ]Nuffield Department of Clinical Neurosciences, Oxford University, John Radcliffe Hospital, Oxford, United Kingdom
                Indiana University, United States of America
                Author notes

                The authors have declared that no competing interests exist.

                Conceived and designed the experiments: RJM MS RJD KJF. Performed the experiments: RJM MS. Analyzed the data: RJM KJF. Contributed reagents/materials/analysis tools: RJM KJF. Wrote the paper: RJM MS RJD KJF.

                Article
                PCOMPBIOL-D-13-01339
                10.1371/journal.pcbi.1003422
                3900375
                24465195

                This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                Page count
                Pages: 8
                Funding
                This work was supported by the Wellcome Trust and Virginia Tech Carilion Research Institute. RJD is supported by a Wellcome Trust Senior Investigator Award 098362/Z/12/Z. The Wellcome Trust Centre for Neuroimaging is supported by core funding from the Wellcome Trust 091593/Z/10/Z. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Biology
                Anatomy and Physiology
                Computational Biology
                Neuroscience
                Computational Neuroscience
                Neuroimaging

                Quantitative & Systems biology

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