title: Trading speed and accuracy by coding time: a coupled-circuit cortical model.
abstract: Our actions take place in space and time, but despite the role of time in decision theory and the growing acknowledgement that the encoding of time is crucial to behaviour, few studies have considered the interactions between neural codes for objects in space and for elapsed time during perceptual decisions. The speed-accuracy trade-off (SAT) provides a window into spatiotemporal interactions. Our hypothesis is that temporal coding determines the rate at which spatial evidence is integrated, controlling the SAT by gain modulation. Here, we propose that local cortical circuits are inherently suited to the relevant spatial and temporal coding. In simulations of an interval estimation task, we use a generic local-circuit model to encode time by 'climbing' activity, seen in cortex during tasks with a timing requirement. The model is a network of simulated pyramidal cells and inhibitory interneurons, connected by conductance synapses. A simple learning rule enables the network to quickly produce new interval estimates, which show signature characteristics of estimates by experimental subjects. Analysis of network dynamics formally characterizes this generic, local-circuit timing mechanism. In simulations of a perceptual decision task, we couple two such networks. Network function is determined only by spatial selectivity and NMDA receptor conductance strength; all other parameters are identical. To trade speed and accuracy, the timing network simply learns longer or shorter intervals, driving the rate of downstream decision processing by spatially non-selective input, an established form of gain modulation. Like the timing network's interval estimates, decision times show signature characteristics of those by experimental subjects. Overall, we propose, demonstrate and analyse a generic mechanism for timing, a generic mechanism for modulation of decision processing by temporal codes, and we make predictions for experimental verification.
authors.affiliation: Department of Biomedical and Molecular Sciences and Center for Neuroscience Studies, Queen's University, Kingston, Ontario, Canada. standage@queensu.ca
authors.forename: Dominic
authors.initials: D
authors.lastname: Standage
authors.forename: Hongzhi
authors.initials: H
authors.lastname: You
authors.forename: Da-Hui
authors.initials: DH
authors.lastname: Wang
authors.forename: Michael C
authors.initials: MC
authors.lastname: Dorris
mesh_terms: D000465:Algorithms; D001519:Behavior; D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D003657:Decision Making; D006801:Humans; D007395:Interneurons / Q000378:metabolism; D007858:Learning; D008959:Models, Neurological; D009474:Neurons / Q000378:metabolism / Q000502:physiology; D010465:Perception; D015203:Reproducibility of Results; D013997:Time Factors