Abstract:Representing others' intentions is central to primate social life. We explored the role of dorsal premotor cortex (PMd) in discriminating between self and others' behavior while two male rhesus monkeys performed a non-match-to-goal task in a monkey-human paradigm. During each trial, two of four potential targets were randomly presented on the right and left parts of a screen, and the monkey or the human was required to choose the one that did not match the previously chosen target. Each agent had to monitor th… Show more
“…Figure 1 illustrates the spatial NMTG (S-NMTG) and object NMTG (O-NMTG) tasks described in a previous work (Cirillo et al., 2018) and in more details in Methods.Figure 1Behavioral TaskTemporal sequence of events of one example trial of the object non-match-to-goal task (O-NMTG). Left panel shows the peripheral targets used during the spatial and object versions of the task.…”
Section: Resultsmentioning
confidence: 99%
“…The database for this study consisted of 328 neurons (210 and 118 from monkey 1 and monkey 2, respectively) that were selected by using the single-unit stability method described in Cirillo et al. (2018).…”
Section: Resultsmentioning
confidence: 99%
“…These studies showed comparable signal durations in PMd and PFdl, in contrast to shorter signal durations observed in ventrolateral prefrontal cortex and the globus pallidus. Using our dataset on the non-match-to-goal (NMTG) task (Cirillo et al., 2018), we addressed the study of the intrinsic timescales of single neurons by computing their intrinsic features during the baseline period. Using the baseline period prevents our analysis from being influenced by relevant task parameters, and leading to define them as “intrinsic timescales.” In particular, we focused on the baseline activity during the holding central stimulus epoch at the beginning of each trial.…”
SummaryOur brain continuously receives information over multiple timescales that are differently processed across areas. In this study, we investigated the intrinsic timescale of neurons in the dorsal premotor cortex (PMd) of two rhesus macaques while performing a non-match-to-goal task. The task rule was to reject the previously chosen target and select the alternative one. We defined the intrinsic timescale as the decay constant of the autocorrelation structure computed during a baseline period of the task. We found that neurons with longer intrinsic timescale tended to maintain a stronger spatial response coding during a delay period. This result suggests that longer intrinsic timescales predict the functional role of PMd neurons in a cognitive task. Our estimate of the intrinsic timescale integrates an existing hierarchical model (Murray et al., 2014), by assigning to PMd a lower position than prefrontal cortex in the hierarchical ordering of the brain areas based on neurons' timescales.
“…Figure 1 illustrates the spatial NMTG (S-NMTG) and object NMTG (O-NMTG) tasks described in a previous work (Cirillo et al., 2018) and in more details in Methods.Figure 1Behavioral TaskTemporal sequence of events of one example trial of the object non-match-to-goal task (O-NMTG). Left panel shows the peripheral targets used during the spatial and object versions of the task.…”
Section: Resultsmentioning
confidence: 99%
“…The database for this study consisted of 328 neurons (210 and 118 from monkey 1 and monkey 2, respectively) that were selected by using the single-unit stability method described in Cirillo et al. (2018).…”
Section: Resultsmentioning
confidence: 99%
“…These studies showed comparable signal durations in PMd and PFdl, in contrast to shorter signal durations observed in ventrolateral prefrontal cortex and the globus pallidus. Using our dataset on the non-match-to-goal (NMTG) task (Cirillo et al., 2018), we addressed the study of the intrinsic timescales of single neurons by computing their intrinsic features during the baseline period. Using the baseline period prevents our analysis from being influenced by relevant task parameters, and leading to define them as “intrinsic timescales.” In particular, we focused on the baseline activity during the holding central stimulus epoch at the beginning of each trial.…”
SummaryOur brain continuously receives information over multiple timescales that are differently processed across areas. In this study, we investigated the intrinsic timescale of neurons in the dorsal premotor cortex (PMd) of two rhesus macaques while performing a non-match-to-goal task. The task rule was to reject the previously chosen target and select the alternative one. We defined the intrinsic timescale as the decay constant of the autocorrelation structure computed during a baseline period of the task. We found that neurons with longer intrinsic timescale tended to maintain a stronger spatial response coding during a delay period. This result suggests that longer intrinsic timescales predict the functional role of PMd neurons in a cognitive task. Our estimate of the intrinsic timescale integrates an existing hierarchical model (Murray et al., 2014), by assigning to PMd a lower position than prefrontal cortex in the hierarchical ordering of the brain areas based on neurons' timescales.
“…The NMTG task is considered to be a task with a high cognitive demand, because the monkeys are required to apply a counterintuitive rule 47,[50][51][52][53][54] and continuously monitor the trials performed, because the correct choice in one trial depends on the previous trial. We showed that this task could be learned using a home-cage training approach.…”
In neurophysiology, nonhuman primates represent an important model for studying the brain. Typically, monkeys are moved from their home cage to an experimental room daily, where they sit in a primate chair and interact with electronic devices. Refining this procedure would make the researchers’ work easier and improve the animals’ welfare. To address this issue, we used home-cage training to train two macaque monkeys in a non-match-to-goal task, where each trial required a switch from the choice made in the previous trial to obtain a reward. The monkeys were tested in two versions of the task, one in which they acted as the agent in every trial and one in which some trials were completed by a “ghost agent”. We evaluated their involvement in terms of their performance and their interaction with the apparatus. Both monkeys were able to maintain a constant involvement in the task with good, stable performance within sessions in both versions of the task. Our study confirms the feasibility of home-cage training and demonstrates that even with challenging tasks, monkeys can complete a large number of trials at a high performance level, which is a prerequisite for electrophysiological studies of monkey behavior.
“…New recording techniques allow for the simultaneous recording of much larger numbers of neurons, in some cases up to hundreds of neurons and across several brain regions (Dotson, Hoffman, Goodell, & Gray, 2017;Nicolelis et al, 2003). The PSTH-based method has been shown to be very sensitive to the number of neurons used, with an increase in accuracy with an increase in the number of neurons considered (Cirillo, Ferrucci, Marcos, Ferraina, & Genovesio, 2018;Falcone, Cirillo, Ferraina, & Genovesio, 2017;Genovesio, Brasted, et al, 2006;Lebedev, Messinger, Kralik, & Wise, 2004;Marcos & Genovesio, 2016;Marcos, Nougaret, Tsujimoto, & Genovesio, 2018;Marcos, Tsujimoto, & Genovesio, 2017). Thus, at least two open questions arise from these new kinds of data sets: (1) Would the two methods reach similar levels of performance for large numbers of neurons, or, even in those cases, would the HMMs always be capable of extracting further information than that obtain from the mean response?…”
Beyond average firing rate, other measurable signals of neuronal activity are fundamental to an understanding of behavior. Recently, hidden Markov models (HMMs) have been applied to neural recordings and have described how neuronal ensembles process information by going through sequences of different states. Such collective dynamics are impossible to capture by just looking at the average firing rate. To estimate how well HMMs can decode information contained in single trials, we compared HMMs with a recently developed classification method based on the peristimulus time histogram (PSTH). The accuracy of the two methods was tested by using the activity of prefrontal neurons recorded while two monkeys were engaged in a strategy task. In this task, the monkeys had to select one of three spatial targets based on an instruction cue and on their previous choice. We show that by using the single trial's neural activity in a period preceding action execution, both models were able to classify the monkeys' choice with an accuracy higher than by chance. Moreover, the HMM was significantly more accurate than the PSTH-based method, even in cases in which the HMM performance was low, although always above chance. Furthermore, the accuracy of both methods was related to the number of neurons exhibiting spatial selectivity within an experimental session. Overall, our study shows that neural activity is better described when not only the mean activity of individual neurons is considered and that therefore, the study of other signals rather than only the average firing rate is fundamental to an understanding of the dynamics of neuronal ensembles.
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