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Techniques for extracting single-trial activity patterns from large-scale neural recordings

Churchland, MM; Yu, BM; Sahani, M; Shenoy, KV; (2007) Techniques for extracting single-trial activity patterns from large-scale neural recordings. CURR OPIN NEUROBIOL , 17 (5) 609 - 618. 10.1016/j.conb.2007.11.001.

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Abstract

Large, chronically implanted arrays of microelectrodes are an increasingly common tool for recording from primate cortex and can provide extracellular recordings from many (order of 100) neurons. While the desire for cortically based motor prostheses has helped drive their development, such arrays also offer great potential to advance basic neuroscience research. Here we discuss the utility of array recording for the study of neural dynamics. Neural activity often has dynamics beyond that driven directly by the stimulus. While governed by those dynamics, neural responses may nevertheless unfold differently for nominally identical trials, rendering many traditional analysis methods ineffective. We review recent studies - some employing simultaneous recording, some not - indicating that such variability is indeed present both during movement generation and during the preceding premotor computations. In such cases, large-scale simultaneous recordings have the potential to provide an unprecedented view of neural dynamics at the level of single trials. However, this enterprise will depend not only on techniques for simultaneous recording but also on the use and further development of analysis techniques that can appropriately reduce the dimensionality of the data, and allow visualization of single-trial neural behavior.

Type:Article
Title:Techniques for extracting single-trial activity patterns from large-scale neural recordings
DOI:10.1016/j.conb.2007.11.001
Keywords:PRIMARY MOTOR CORTEX, DIRECT CORTICAL CONTROL, MOVEMENT DIRECTION, FEEDBACK-CONTROL, PREMOTOR CORTEX, REACTION-TIME, SPIKE TRAINS, POPULATION, VARIABILITY, DYNAMICS
UCL classification:UCL > School of Life and Medical Sciences > Faculty of Life Sciences > Gatsby Computational Neuroscience Unit

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