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Decoding the allocation of visual attention from prefrontal neural assemblies in behaving primates

33.522, Sunday, 18-May, 8:30 am - 12:30 pm, Pavilion
Session: Attention: Neural mechanisms and modeling

Sebastien Tremblay1, Florian Pieper2, Adam Sachs3, Julio Martinez-Trujillo1; 1Cognitive Neurophysiology Laboratory, Department of Physiology, McGill University, Montreal, Canada, 2Institute for Neuro- & Pathophysiology, University Medical Center Hamburg-Eppendorf (UKE), Hamburg, Germany, 3Division of Neurosurgery, Department of Surgery, The Ottawa Hospital Research Institute, University of Ottawa, Ottawa, Canada

The primate prefrontal cortex is thought to play an important role in intelligent goal-directed behaviour. Single neurons in different regions of the PFC are tuned for the allocation of attention as well as for the final position of saccades. Here we show that the activity of a small population of simultaneously recorded neurons from macaque PFC area 8a can be reliably decoded to signal the allocation of attention to one of four Gabor stimuli presented on a computer screen with 71% accuracy, and the goal of a saccade to the same stimulus with 95% accuracy. The presence of a transient change in one of the unattended distracters slightly decreased the coding accuracy by 25%, demonstrating that the encoding was robust to interference by transient distracter changes. Moreover, the population code was equally reliable when we used the pooled multiunit activity of single electrodes rather than the sorted single unit activity. Importantly, the code was constant across a timespan of multiple weeks, suggesting a stable functional network architecture underlying the coding of attention and saccade goal. Our results demonstrate that the activity of a small population of neurons, distributed over an area of ~16 mm2 of PFC, contains sufficient information to decode the allocation of spatial attention as well as the goal of a saccade with high accuracy, robustness, and stability over time. They suggest that PFC area 8a could be a target for brain machine interfaces (BMI) that take into account the relevance of environmental stimuli to produce goal-oriented behaviour.

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