Dotson NM, Salazar RF, Goodell AB, Hoffman SJ, Gray CM (2015). Methods, caveats, and the future of large-scale microelectrode recordings in the non-human primate...

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Dotson NM, Salazar RF, Goodell AB, Hoffman SJ, Gray CM (2015). Methods, caveats, and the future of large-scale microelectrode recordings in the non-human primate. Frontiers in Systems Neuroscience, 9, 149.


Cognitive processes play out on massive brain-wide networks, which produce widely distributed patterns of activity. Capturing these activity patterns requires tools that are able to simultaneously measure activity from many distributed sites with high spatiotemporal resolution. Unfortunately, current techniques with adequate coverage do not provide the requisite spatiotemporal resolution. Large-scale microelectrode recording devices, with dozens to hundreds of microelectrodes capable of simultaneously recording from nearly as many cortical and subcortical areas, provide a potential way to minimize these tradeoffs. However, placing hundreds of microelectrodes into a behaving animal is a highly risky and technically challenging endeavor that has only been pursued by a few groups. Recording activity from multiple electrodes simultaneously also introduces several statistical and conceptual dilemmas, such as the multiple comparisons problem and the uncontrolled stimulus response problem. In this perspective article, we discuss some of the techniques that we, and others, have developed for collecting and analyzing large-scale data sets, and address the future of this emerging field.

Markowitz DA, Wong YT, Gray CM, Pesaran B (2011). Optimizing the decoding of movement goals from local field potentials in macaque cortex. J Neuroscience, 31:18412-18422.

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The successful development of motor neuroprosthetic devices hinges on the ability to accurately and reliably decode signals from the brain. Motor neuroprostheses are widely investigated in behaving non-human primates, but technical constraints have limited progress in optimizing performance. In particular, the organization of movement-related neuronal activity across cortical layers remains poorly understood due, in part, to the widespread use of fixed-geometry multielectrode arrays. In this study, we use chronically implanted multielectrode arrays with individually movable electrodes to examine how the encoding of movement goals depends on cortical depth. In a series of recordings spanning several months, we varied the depth of each electrode in the prearcuate gyrus of frontal cortex in two monkeys as they performed memory-guided eye movements. We decode eye movement goals from local field potentials (LFPs) and multiunit spiking activity recorded across a range of depths up to 3 mm from the cortical surface. We show that both LFP and multiunit signals yield the highest decoding performance at superficial sites, within 0.5 mm of the cortical surface, while performance degrades substantially at sites deeper than 1 mm. We also analyze performance by varying bandpass filtering characteristics and simulating changes in microelectrode array channel count and density. The results indicate that the performance of LFP-based neuroprostheses strongly depends on recording configuration and that recording depth is a critical parameter limiting system performance.