Heydari, M. R. | Search bioRxiv

bioRxiv2023

Prefrontal cortex signals value category while basal ganglia represent learned values in value learning

Value learning can be variable for objects with the exact same reward but the underlying neural mechanism for such variability is not known. We have addressed this question by recording single-unit activity in the prefrontal cortex (PFC) and substantia nigra reticulata (SNr), two key nodes in cortex-basal ganglia circuitry with crucial roles in value learning, as macaque monkeys learned to associate novel objects with either high or low rewards. Estimating the trial by trial learned values based on choice performance, revealed stark differences between learned values across objects with the same reward outcome. Importantly, while PFC neurons rapidly learned to differentiate objects based on their value category, the firing in SNr correlated with the variability in learned values within a value category. Our results suggest that the variation in objects learned values is more likely to be a readout of SNr firing while PFC may provide a top-down teaching signal to basal ganglia to demarcate the value categories.

neuroscience↗

bioRxiv2019

Introducing Rhythmic Sinusoidal Amplitude-Modulated Auditory Stimuli with Multiple Message Frequency Coding for Fatigue Reduction in Normal Subjects: An EEG Study

Many of the brain-computer interface (BCI) systems depend on the users voluntary eye movements. However, voluntary eye movement is impaired in people with some neurological disorders. Since their auditory system is intact, auditory paradigms are getting more patronage from researchers. However, lack of appropriate signal-to-noise ratio in auditory BCI necessitates using long signal processing windows to achieve acceptable classification accuracy at the expense of losing information transfer rate. Because users eagerly listen to their interesting stimuli, the corresponding classification accuracy can be enhanced without lengthening of the signal processing windows. In this study, six sinusoidal amplitude-modulated auditory stimuli with multiple message frequency coding have been proposed to evaluate two hypotheses: 1) these novel stimuli provide high classification accuracies (greater than 70%), 2) the novel rhythmic stimuli set reduces the subjects fatigue compared to its simple counterpart. We recorded EEG from nineteen normal subjects (twelve female). Five-fold cross-validated naive Bayes classifier classified EEG signals with respect to power spectral density at message frequencies, Pearsons correlation coefficient between the responses and stimuli envelopes, canonical correlation coefficient between the responses and stimuli envelopes. Our results show that each stimuli set elicited highly discriminative responses according to all the features. Moreover, compared to the simple stimuli set, listening to the rhythmic stimuli set caused significantly lower subjects fatigue. Thus, it is worthwhile to test these novel stimuli in a BCI experiment to enhance the number of commands and reduce the subjects fatigue. Significance StatementAuditory BCI users eagerly listen to the stimuli they are interested in. Thus, response classification accuracy may be enhanced without the need for trial lengthening. Since humans enjoy listening to rhythmic sounds, this study was carried out for introducing novel rhythmic sinusoidal amplitude-modulated auditory stimuli with multiple message frequency coding. Our results show that each stimuli set evoked reliably discriminative responses according to all the features, and rhythmic stimuli set caused significantly lower fatigue in subjects. Thus, it is worthwhile to test these novel stimuli in a BCI study to increase the number of commands (by NN permutations of just N message frequencies) and reduce the subjects fatigue.

neuroscience↗