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Use of multi-electrode array recordings in studies of network synaptic plasticity in both time and space.

ABSTRACT: Simultaneous multisite recording using multi-electrode arrays (MEAs) in cultured and acutely-dissociated brain slices and other tissues is an emerging technique in the field of network electrophysiology. Over the past 40 years, great efforts have been made by both scientists and commercial concerns, to advance this technique. The MEA technique has been widely applied to many regions of the brain, retina, heart and smooth muscle in various studies at the network level. The present review starts from the development of MEA techniques and their uses in brain preparations, and then specifically concentrates on the use of MEA recordings in studies of synaptic plasticity at the network level in both the temporal and spatial domains. Because the MEA technique helps bridge the gap between single-cell recordings and behavioral assays, its wide application will undoubtedly shed light on the mechanisms underlying brain functions and dysfunctions at the network level that remained largely unknown due to the technical difficulties before it matured.

SUBMITTER: Liu MG

PROVIDER: S-EPMC5561892 | biostudies-other | 2012 Aug

REPOSITORIES: biostudies-other

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Project description:It is generally accepted that the number of neurons in a given brain area far exceeds the number of neurons needed to carry any specific function controlled by that area. For example, motor areas of the human brain contain tens of millions of neurons that control the activation of tens or at most hundreds of muscles. This massive redundancy implies the covariation of many neurons, which constrains the population activity to a low-dimensional manifold within the space of all possible patterns of neural activity. To gain a conceptual understanding of the complexity of the neural activity within a manifold, it is useful to estimate its dimensionality, which quantifies the number of degrees of freedom required to describe the observed population activity without significant information loss. While there are many algorithms for dimensionality estimation, we do not know which are well suited for analyzing neural activity. The objective of this study was to evaluate the efficacy of several representative algorithms for estimating the dimensionality of linearly and nonlinearly embedded data. We generated synthetic neural recordings with known intrinsic dimensionality and used them to test the algorithms' accuracy and robustness. We emulated some of the important challenges associated with experimental data by adding noise, altering the nature of the embedding of the low-dimensional manifold within the high-dimensional recordings, varying the dimensionality of the manifold, and limiting the amount of available data. We demonstrated that linear algorithms overestimate the dimensionality of nonlinear, noise-free data. In cases of high noise, most algorithms overestimated the dimensionality. We thus developed a denoising algorithm based on deep learning, the "Joint Autoencoder", which significantly improved subsequent dimensionality estimation. Critically, we found that all algorithms failed when the intrinsic dimensionality was high (above 20) or when the amount of data used for estimation was low. Based on the challenges we observed, we formulated a pipeline for estimating the dimensionality of experimental neural data.

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Use of multi-electrode array recordings in studies of network synaptic plasticity in both time and space.

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