Population Of Prefrontal Neurons Research Articles

Neuronal population correlates of target selection and distractor filtering

Frontal Eye Field (FEF) neurons discriminate between relevant and irrelevant visual stimuli and their response magnitude predicts conscious perception. How this is reflected in the spatial representation of a visual stimulus at the neuronal population level is unknown. We recorded neuronal population activity in the FEF while monkeys were performing a forced choice cued detection task with identical target and distractor stimuli. We quantified, using machine learning techniques, estimates of target and distractor location from FEF population multiunit activities. We found that the FEF population activity provides a precise single trial estimate of reported stimuli locations. Importantly, the closer this prefrontal population single trial estimate is to the veridical stimulus location, the higher the probability that the target or the distractor is reported as perceived. We show that stimulus perception is rescued by the estimate of attention allocation specifically when the latter is close enough to the actual stimulus location, thus indicating a partial independence between attention and perception. Overall, we thus show that how and what we perceive of our environment depends on the spatial precision with which this environment is coded by prefrontal neuronal populations.

Comparison of Classifiers for Decoding Sensory and Cognitive Information from Prefrontal Neuronal Populations

Decoding neuronal information is important in neuroscience, both as a basic means to understand how neuronal activity is related to cerebral function and as a processing stage in driving neuroprosthetic effectors. Here, we compare the readout performance of six commonly used classifiers at decoding two different variables encoded by the spiking activity of the non-human primate frontal eye fields (FEF): the spatial position of a visual cue, and the instructed orientation of the animal's attention. While the first variable is exogenously driven by the environment, the second variable corresponds to the interpretation of the instruction conveyed by the cue; it is endogenously driven and corresponds to the output of internal cognitive operations performed on the visual attributes of the cue. These two variables were decoded using either a regularized optimal linear estimator in its explicit formulation, an optimal linear artificial neural network estimator, a non-linear artificial neural network estimator, a non-linear naïve Bayesian estimator, a non-linear Reservoir recurrent network classifier or a non-linear Support Vector Machine classifier. Our results suggest that endogenous information such as the orientation of attention can be decoded from the FEF with the same accuracy as exogenous visual information. All classifiers did not behave equally in the face of population size and heterogeneity, the available training and testing trials, the subject's behavior and the temporal structure of the variable of interest. In most situations, the regularized optimal linear estimator and the non-linear Support Vector Machine classifiers outperformed the other tested decoders.

Prediction of multi-dimensional decision-making based on the single-trial activities of a population of prefrontal neurons

Prediction of multi-dimensional decision-making based on the single-trial activities of a population of prefrontal neurons

What information is represented by prefrontal neuronal activity?

Working memory is a mechanism for short-term active storage of information and for processing stored information. Although evidence for temporary storage mechanisms of information has been accumulated, little is known about neuronal mechanisms for processing information. To understand how information is processed in the nervous system, we first need to know what information single-neuron activity represents, then examine how represented information by single-neuron activities changes along the temporal sequence of the trial. We used two kinds of culomotor delayed-response (ODR) tasks and examined what information prefrontal single-neuron activity represents. We found that all cue-period activity encoded cue positions. However, among neurons, which exhibited delay-period activity, 86% encoded cue positions, while 13% encoded saccade directions. In addition, among neurons exhibiting oculomotor activity, 58% encoded saccade directions, while 35% encoded cue positions. To visualize temporal patterns of changes of information represented by a population of prefrontal neurons, we analyzed single-neuron activities using a population vector analysis. As a result, we found that information represented by a population of PFC neurons changes gradually during the delay period from information for visual cue to that for saccade.