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Integration of optogenetics with complementary methodologies in systems neuroscience.

ABSTRACT: Modern optogenetics can be tuned to evoke activity that corresponds to naturally occurring local or global activity in timing, magnitude or individual-cell patterning. This outcome has been facilitated not only by the development of core features of optogenetics over the past 10 years (microbial-opsin variants, opsin-targeting strategies and light-targeting devices) but also by the recent integration of optogenetics with complementary technologies, spanning electrophysiology, activity imaging and anatomical methods for structural and molecular analysis. This integrated approach now supports optogenetic identification of the native, necessary and sufficient causal underpinnings of physiology and behaviour on acute or chronic timescales and across cellular, circuit-level or brain-wide spatial scales.

SUBMITTER: Kim CK

PROVIDER: S-EPMC5708544 | biostudies-literature | 2017 Mar

REPOSITORIES: biostudies-literature

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Integration of optogenetics with complementary methodologies in systems neuroscience.

Kim Christina K CK Adhikari Avishek A Deisseroth Karl K

Nature reviews. Neuroscience 20170301 4

Modern optogenetics can be tuned to evoke activity that corresponds to naturally occurring local or global activity in timing, magnitude or individual-cell patterning. This outcome has been facilitated not only by the development of core features of optogenetics over the past 10 years (microbial-opsin variants, opsin-targeting strategies and light-targeting devices) but also by the recent integration of optogenetics with complementary technologies, spanning electrophysiology, activity imaging an ...[more]

PMID: 28303019

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Project description:According to complementary learning systems theory, integrating new memories into the neocortex of the brain without interfering with what is already known depends on a gradual learning process, interleaving new items with previously learned items. However, empirical studies show that information consistent with prior knowledge can sometimes be integrated very quickly. We use artificial neural networks with properties like those we attribute to the neocortex to develop an understanding of the role of consistency with prior knowledge in putatively neocortex-like learning systems, providing new insights into when integration will be fast or slow and how integration might be made more efficient when the items to be learned are hierarchically structured. The work relies on deep linear networks that capture the qualitative aspects of the learning dynamics of the more complex nonlinear networks used in previous work. The time course of learning in these networks can be linked to the hierarchical structure in the training data, captured mathematically as a set of dimensions that correspond to the branches in the hierarchy. In this context, a new item to be learned can be characterized as having aspects that project onto previously known dimensions, and others that require adding a new branch/dimension. The projection onto the known dimensions can be learned rapidly without interleaving, but learning the new dimension requires gradual interleaved learning. When a new item only overlaps with items within one branch of a hierarchy, interleaving can focus on the previously known items within this branch, resulting in faster integration with less interleaving overall. The discussion considers how the brain might exploit these facts to make learning more efficient and highlights predictions about what aspects of new information might be hard or easy to learn. This article is part of the Theo Murphy meeting issue 'Memory reactivation: replaying events past, present and future'.

Dataset Information

Integration of optogenetics with complementary methodologies in systems neuroscience.

Publications

Integration of optogenetics with complementary methodologies in systems neuroscience.

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