Byounghoon Kim

Saccade Target Selection in the Superior Colliculus: A Signal Detection Theory Approach

How the brain selects one action from among multiple options is unknown. A main tenet of signal detection theory (SDT) is that sensory stimuli are represented as noisy information channels. Therefore, the accuracy of selection might be predicted by how well neuronal activity representing alternatives can be distinguished. Here, we apply an SDT framework to a motor system by recording from superior colliculus (SC) neurons during performance of a color, oddball selection task. We recorded from sets of four neurons simultaneously, each of the four representing one of the four possible targets. Because the electrode placement constrained the position of the stimuli in the visual field, the stimulus arrangement varied across experiments. This variability in stimulus arrangement led to variability in choices allowing us to explore the relationship between SC neuronal activity and performance accuracy. SC target neurons had higher levels of discharge than SC distractor neurons in subsets of trials when selection performance was very accurate. In subsets of trials when performance was poor, the discharge level decreased in target neurons and increased in distractor neurons. Accurate performance was associated with larger separations between neuronal activity from targets and distractors as quantified by the receiver operating characteristic (ROC) area and d′ (an index of discriminability). Poorer performance was associated with less separation of target and distractor neuronal activity. ROC area and d′ scaled approximately linearly with performance accuracy. Furthermore, ROC area and d′ increased as saccade onset approached. Together, the results indicate that SC buildup neuronal activity signals the saccadic eye movement decision.

Objective measures of health and well-being in laboratory rhesus monkeys (Macaca mulatta)

Background  Non‐human primates are an invaluable part of biomedical research. Strict regulations insure animals have a maximum likelihood of well‐being and optimum health during the course of experimental procedures. Objective assessment of well‐being is a critical component of these assurances.

An automated food delivery system for behavioral and neurophysiological studies of learning and memory in freely moving monkeys

We describe a custom-built feeder based on stepping motor technology controlled by a laboratory computer. The feeder dispenses a wide range of foods: any fruit, vegetable, or nut. The feeder allows the investigator to reward monkeys with different foods within a single experimental day. The monkey’s motivation to perform tasks is high and does not rely upon food regulation. The avoidance of regulation, as well as the palatability and variety of the rewards dispensed by our device, distinguishes it from commercially available products. We also describe the use of the feeder in the context of novel behavioral and neurophysiological studies in freely moving monkeys.

Real-time experimental control using network-based parallel processing

Modern neuroscience research often requires the coordination of multiple processes such as stimulus generation, real-time experimental control, as well as behavioral and neural measurements. The technical demands required to simultaneously manage these processes with high temporal fidelity limits the number of labs capable of performing such work. Here we present an open-source network-based parallel processing framework that eliminates these barriers. The Real-Time Experimental Control with Graphical User Interface (REC-GUI) framework offers multiple advantages: (i) a modular design agnostic to coding language(s) and operating system(s) that maximizes experimental flexibility and minimizes researcher effort, (ii) simple interfacing to connect measurement and recording devices, (iii) high temporal fidelity by dividing task demands across CPUs, and (iv) real-time control using a fully customizable and intuitive GUI. Testing results demonstrate that the REC-GUI framework facilitates technically demanding, behavior-contingent neuroscience research. Sample code and hardware configurations are downloadable, and future developments will be regularly released.