Modeling Saccadic Action Selection: Cortical and Basal Ganglia Signals Coalesce in the Superior Colliculus - Coe, Brian C.; Trappenberg, Thomas; Munoz, Douglas P. (Frontiers in Systems Neuroscience, 2019)
The distributed nature of information processing in the brain creates a complex variety of decision making behaviour. Likewise, computational models of saccadic decision making behavior are numerous and diverse. Here we present a generative model of saccadic action selection in the context of competitive decision making in the superior colliculus (SC) in order to develop diagnostic tools. Our aim is to integrate the dynamics of major pathways to the SC into the model and to test if systematic variations can better replicate behavioural variations found in human behavior. This model was tasked with performing pro- and anti-saccades in order to replicate specific attributes of human saccade behavior. In these tasks participants are instructed to either look toward (pro-saccade, well-practiced automated response) or away from (anti-saccade, combination of inhibitory and voluntary responses) a peripheral visual stimulus. The behaviors of interest are express latency pro-saccades, express latency direction errors, and regular latency direction errors. A direction error is when participants look toward the stimulus in the anti-saccade task. To gain a better understanding of the underlying neural processes that lead to saccadic action selection and response inhibition, we implemented 8 inputs inspired by systems neuroscience. Unlike models that emulated cell types, these inputs were inspired by sensory, automated, voluntary, and inhibitory components of cortical and basal ganglia activity that are known to coalesce in the intermediate layers of the superior colliculus: a key integration center for saccade commands. This improved model produced bimodal reaction time distributions, where express and regular latency saccades had distinct modes, for both pro-saccades and direction errors in the anti-saccade task. Because this model’s inputs represented different components of neural activity, insight was gained into the neural circuits guiding each behavior. For example, express latency direction errors were due to a lack of pre-emptive fixation and inhibitory activity, whereas regular latency direction errors were due to automated motor drives overriding voluntary motor signals. While there have been previous models emulating aspects of these behavioral findings, the focus of the simulations here is on the interaction of a wide variety of physiologically-based information integration and the corresponding behavioral variability.