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Amirsaman Sajad, Jeffrey Schall; MICROCIRCUITRY OF VISUAL PERFORMANCE MONITORING. Journal of Vision 2017;17(10):1150. doi: 10.1167/17.10.1150.
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© ARVO (1962-2015); The Authors (2016-present)
Being error prone, the context and consequences of visual behavior must be monitored to achieve goals. Previous work in macaque monkeys performing a saccade countermanding task has found that neural spiking and field potential signals occur in the supplementary eye field (SEF) and anterior cingulate cortex when errors are made, when reward is expected and when conflict between competing saccade plans arises. We will now report the laminar organization of these visual performance monitoring signals in neurophysiological data collected with linear electrode arrays (U-probe) sampling all layers of the SEF. We isolated 293 cells in 16 penetrations from two monkeys. Of these, 273 were modulated in the period between target and 200 ms after reinforcement. Of the 273, 238 had at least one of 5 signal types: visual (32%), motor (52%), error (34%), reinforcement (63%), and conflict (18%) with the majority multiplexed. We have found that neurons with particular signals are concentrated in specific layers. Neurons signaling saccade plan conflict are concentrated in upper layers (Fisher exact test, p = 0.025). Similarly, the incidence of error (Fisher p = 0.030), reward (Fisher p = 0.010) and visual (Fisher p < 0.001) neurons varied across layers, but perisaccadic (Fisher p = 0.600) neurons did not. We have also discovered reproducibility of discharge profiles across repeated penetrations but pronounced variability in penetrations at different locations in different monkeys. These findings contribute to the evaluation of alternative hypotheses about medial frontal function, to understanding the contributions of laminar-specific cortical/subcortical and feedforward/ feedback processes, to constraining circuit-level models of executive control and to guiding forward and inverse modeling solutions of the ERN. (We thank D. Godlove for sharing data)
Meeting abstract presented at VSS 2017
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