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      Habituation based synaptic plasticity and organismic learning in a quantum perovskite

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          Abstract

          A central characteristic of living beings is the ability to learn from and respond to their environment leading to habit formation and decision making. This behavior, known as habituation, is universal among all forms of life with a central nervous system, and is also observed in single-cell organisms that do not possess a brain. Here, we report the discovery of habituation-based plasticity utilizing a perovskite quantum system by dynamical modulation of electron localization. Microscopic mechanisms and pathways that enable this organismic collective charge-lattice interaction are elucidated by first-principles theory, synchrotron investigations, ab initio molecular dynamics simulations, and in situ environmental breathing studies. We implement a learning algorithm inspired by the conductance relaxation behavior of perovskites that naturally incorporates habituation, and demonstrate learning to forget: a key feature of animal and human brains. Incorporating this elementary skill in learning boosts the capability of neural computing in a sequential, dynamic environment.

          Abstract

          Habituation is a learning mechanism that enables control over forgetting and learning. Zuo, Panda et al., demonstrate adaptive synaptic plasticity in SmNiO 3 perovskites to address catastrophic forgetting in a dynamic learning environment via hydrogen-induced electron localization.

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          Efficient iterative schemes forab initiototal-energy calculations using a plane-wave basis set

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            Generalized Gradient Approximation Made Simple.

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              Competitive Hebbian learning through spike-timing-dependent synaptic plasticity.

              Hebbian models of development and learning require both activity-dependent synaptic plasticity and a mechanism that induces competition between different synapses. One form of experimentally observed long-term synaptic plasticity, which we call spike-timing-dependent plasticity (STDP), depends on the relative timing of pre- and postsynaptic action potentials. In modeling studies, we find that this form of synaptic modification can automatically balance synaptic strengths to make postsynaptic firing irregular but more sensitive to presynaptic spike timing. It has been argued that neurons in vivo operate in such a balanced regime. Synapses modifiable by STDP compete for control of the timing of postsynaptic action potentials. Inputs that fire the postsynaptic neuron with short latency or that act in correlated groups are able to compete most successfully and develop strong synapses, while synapses of longer-latency or less-effective inputs are weakened.
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                Author and article information

                Contributors
                kaushik@purdue.edu
                shriram@purdue.edu
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                14 August 2017
                14 August 2017
                2017
                : 8
                : 240
                Affiliations
                [1 ]ISNI 0000 0004 1937 2197, GRID grid.169077.e, School of Materials Engineering, , Purdue University, ; West Lafayette, Indiana 47907 USA
                [2 ]ISNI 0000 0004 1937 2197, GRID grid.169077.e, School of Electrical and Computer Engineering, , Purdue University, ; West Lafayette, Indiana 47907 USA
                [3 ]ISNI 0000 0004 1936 8796, GRID grid.430387.b, Department of Physics and Astronomy, , Rutgers University, ; Piscataway, New Jersey 08854 USA
                [4 ]ISNI 0000 0001 2341 2786, GRID grid.116068.8, Department of Physics, , Massachusetts Institute of Technology, ; Cambridge, Massachusetts 02139 USA
                [5 ]ISNI 0000 0001 2188 4229, GRID grid.202665.5, National Synchrotron Light Source II, , Brookhaven National Laboratory, ; Upton, New York 11973 USA
                [6 ]ISNI 0000 0001 1939 4845, GRID grid.187073.a, X-ray Science Division, Advanced Photon Source, , Argonne National Laboratory, ; Argonne, Illinois 60439 USA
                [7 ]ISNI 0000 0001 1939 4845, GRID grid.187073.a, Center for Nanoscale Materials, , Argonne National Laboratory, ; Argonne, Illinois 60439 USA
                Author information
                http://orcid.org/0000-0002-4167-6782
                Article
                248
                10.1038/s41467-017-00248-6
                5556077
                28808316
                a91acada-7364-4796-9eba-e2517988e7a0
                © The Author(s) 2017

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 11 March 2017
                : 12 June 2017
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