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.

We describe the design and performance of an electromechanical system for conducting multineuron recording experiments in alert non-human primates. The system is based on a simple design, consisting of a microdrive, control electronics, software, and a unique type of recording chamber. The microdrive consists of an aluminum frame, a set of eight linear actuators driven by computer-controlled miniature stepping motors, and two printed circuit boards (PCBs) that provide connectivity to the electrodes and the control electronics. The control circuitry is structured around an Atmel RISC-based microcontroller, which sends commands to as many as eight motor control cards, each capable of controlling eight motors. The microcontroller is programmed in C and uses serial communication to interface with a host computer. The graphical user interface for sending commands is written in C and runs on a conventional personal computer. The recording chamber is low in profile, mounts within a circular craniotomy, and incorporates a removable internal sleeve. A replaceable Sylastic membrane can be stretched across the bottom opening of the sleeve to provide a watertight seal between the cranial cavity and the external environment. This greatly reduces the susceptibility to infection, nearly eliminates the need for routine cleaning, and permits repeated introduction of electrodes into the brain at the same sites while maintaining the watertight seal. The system is reliable, easy to use, and has several advantages over other commercially available systems with similar capabilities.

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.

One of the outstanding problems in the sorting of neuronal spike trains is the resolution of overlapping spikes. Resolving these spikes can significantly improve a range of analyses, such as response variability, correlation, and latency. In this paper, we describe a partially automated method that is capable of resolving overlapping spikes. After constructing template waveforms for well-isolated and distinct single units, we generated pair-wise combinations of those templates at all possible time shifts from each other. Subsequently, overlapping waveforms were identified by cluster analysis, and then assigned to their respective single-unit combinations. We examined the performance of this method using simulated data from an earlier study, and found that we were able to resolve an average of 83% of the overlapping waveforms across various signal-to-noise ratios, an improvement of approximately 32% over the results reported in the earlier study. When applied to additional simulated data sets generated from single-electrode and tetrode recordings, we were able to resolve 91% of the overlapping waveforms with a false positive rate of 0.19% for single-electrode data, and 95% of the overlapping waveforms with a false positive rate of 0.27% for tetrode data. We also applied our method to electrode and tetrode data recorded from the primary visual cortex, and the results obtained for these datasets suggest that our method provides an efficient means of sorting overlapping waveforms. This method can easily be added as an extra step to commonly used spike sorting methods, such as KlustaKwik and MClust software packages, and can be applied to datasets that have already been sorted using these methods.