Feature-based visual short-term memory is known to engage both sensory and association cortices. However, the extent of the participating circuit and the neural mechanisms underlying memory maintenance is still a matter of vigorous debate. To address these questions, we recorded neuronal activity from 42 cortical areas in monkeys performing a feature-based visual short-term memory task and an interleaved fixation task. We find that task-dependent differences in firing rates are widely distributed throughout the cortex, while stimulus-specific changes in firing rates are more restricted and hierarchically organized. We also show that microsaccades during the memory delay encode the stimuli held in memory and that units modulated by microsaccades are more likely to exhibit stimulus specificity, suggesting that eye movements contribute to visual short-term memory processes. These results support a framework in which most cortical areas, within a modality, contribute to mnemonic representations at timescales that increase along the cortical hierarchy.

Multi-electrode recordings in the non-human primate provide a critical method for measuring the widely distributed activity patterns that underlie brain function. However, common techniques rely on small, often immovable arrays, or microdrives, that are only capable of manipulating a small number of closely spaced probes. These techniques restrict the number of cortical areas that can be simultaneously sampled and are typically not capable of reaching subcortical targets. To overcome these limitations, we developed a large-scale, semi-chronic microdrive recording system with up to 256 independently movable microelectrodes spanning an entire cerebral hemisphere. The microdrive system is hermetically sealed, free of internal connecting wires, and has been used to simultaneously record from up to 37 cortical and subcortical areas in awake behaving monkeys for up to 9 months. As a proof of principle, we demonstrate the capability of this technique to address network-level questions using a graph theoretic analysis of functional connectivity data.

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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.

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