Theta (2–8 Hz), alpha (8–12 Hz), beta (12–35 Hz), and gamma (>35 Hz) rhythms are ubiquitous in the cortex. However, there is little understanding of whether they have similar properties and functions in different cortical areas because they have rarely been compared across them. We record neuronal spikes and local field potentials simultaneously at several levels of the cortical hierarchy in monkeys. Theta, alpha, beta, and gamma oscillations had similar relationships to spiking activity in visual, parietal, and prefrontal cortices. However, the frequencies in all bands increased up the cortical hierarchy. These results suggest that these rhythms have similar inhibitory and excitatory functions across the cortex. We discuss how the increase in frequencies up the cortical hierarchy may help sculpt cortical flow and processing.

Large-scale neuronal recording techniques have enabled discoveries of population-level mechanisms for neural computation. However, it is not clear how these mechanisms form by trial-and-error learning. In this article, we present an initial effort to characterize the population activity in monkey prefrontal cortex (PFC) and hippocampus (HPC) during the learning phase of a paired-associate task. To analyze the population data, we introduce the normalized distance, a dimensionless metric that describes the encoding of cognitive variables from the geometrical relationship among neural trajectories in state space. It is found that PFC exhibits a more sustained encoding of the visual stimuli, whereas HPC only transiently encodes the identity of the associate stimuli. Surprisingly, after learning, the neural activity is not reorganized to reflect the task structure, raising the possibility that learning is accompanied by some “silent” mechanism that does not explicitly change the neural representations. We did find partial evidence on the learning-dependent changes for some of the task variables. This study shows the feasibility of using normalized distance as a metric to characterize and compare population-level encoding of task variables and suggests further directions to explore learning-dependent changes in the neural circuits.

Cognitive theories suggest that working memory maintains not only the identity of recently presented stimuli but also a sense of the elapsed time since the stimuli were presented. Previous studies of the neural underpinnings of working memory have focused on sustained firing, which can account for maintenance of the stimulus identity, but not for representation of the elapsed time. We analyzed single-unit recordings from the lateral prefrontal cortex of macaque monkeys during performance of a delayed match-to-category task. Each sample stimulus triggered a consistent sequence of neurons, with each neuron in the sequence firing during a circumscribed period. These sequences of neurons encoded both stimulus identity and elapsed time. The encoding of elapsed time became less precise as the sample stimulus receded into the past. These findings suggest that working memory includes a compressed timeline of what happened when, consistent with long-standing cognitive theories of human memory.

We examined whether PFC neuron activity reflects categorical decisions in monkeys categorizing ambiguous stimuli. A morphing system was used to systematically vary stimulus shape and precisely define category boundaries. Ambiguous stimuli were centered on a category boundary, that is, they were a mix of 50% of two prototypes and therefore had no category information, so monkeys guessed at their category membership. We found that the monkeys' trial-by-trial decision about the category membership of an ambiguous image was reflected in PFC activity. Activity to the same ambiguous image differed significantly, depending on which category the monkey had assigned it to. This effect only occurred when that scheme was behaviorally relevant. These indicate that PFC activity reflects categorical decisions.

Previous work has shown that neurons in the PFC show selectivity for learned categorical groupings. In contrast, brain regions lower in the visual hierarchy, such as inferior temporal cortex, do not seem to favor category information over information about physical appearance. However, the role of premotor cortex (PMC) in categorization has not been studied, despite evidence that PMC is strongly engaged by well-learned tasks and reflects learned rules. Here, we directly compare PFC neurons with PMC neurons during visual categorization. Unlike PFC neurons, relatively few PMC neurons distinguished between categories of visual images during a delayed match-to-category task. However, despite the lack of category information in the PMC, more than half of the neurons in both PFC and PMC reflected whether the category of a test image did or did not match the category of a sample image (i.e., had match information). Thus, PFC neurons represented all variables required to solve the cognitive problem, whereas PMC neurons instead represented only the final decision variable that drove the appropriate motor action required to obtain a reward. This dichotomy fits well with PFC's hypothesized role in learning arbitrary information and directing behavior as well as the PMC's role in motor planning.

The PFC plays a central role in our ability to learn arbitrary rules, such as “green means go.” Previous experiments from our laboratory have used conditional association learning to show that slow, gradual changes in PFC neural activity mirror monkeys' slow acquisition of associations. These previous experiments required monkeys to repeatedly reverse the cue–saccade associations, an ability known to be PFC-dependent. We aimed to test whether the relationship between PFC neural activity and behavior was due to the reversal requirement, so monkeys were trained to learn several new conditional cue–saccade associations without reversing them. Learning-related changes in PFC activity now appeared earlier and more suddenly in correspondence with similar changes in behavioral improvement. This suggests that learning of conditional associations is linked to PFC activity regardless of whether reversals are required. However, when previous learning does not need to be suppressed, PFC acquires associations more rapidly.

The prefrontal cortex is believed to be important for cognitive control, working memory, and learning. It is known to play an important role in the learning and execution of conditional visuomotor associations, a cognitive task in which stimuli have to be associated with actions by trial-and-error learning. In our modeling study, we sought to integrate several hypotheses on the function of the prefrontal cortex using a computational model, and compare the results to experimental data. We constructed a module of prefrontal cortex neurons exposed to two different inputs, which we envision to originate from the inferotemporal cortex and the basal ganglia. We found that working memory properties do not describe the dominant dynamics in the prefrontal cortex, but the activation seems to be transient, probably progressing along a pathway from sensory to motor areas. During the presentation of the cue, the dynamics of the prefrontal cortex is bistable, yielding a distinct activation for correct and error trails. We find that a linear change in network parameters relates to the changes in neural activity in consecutive correct trials during learning, which is important evidence for the underlying learning mechanisms.

The ability to use abstract rules or principles allows behavior to generalize from specific circumstances. We have previously shown that such rules are encoded in the lateral prefrontal cortex (PFC) and premotor cortex (PMC). Here, we extend these investigations to two other areas directly connected with the PFC and the PMC, the inferior temporal cortex (ITC) and the dorsal striatum (STR). Monkeys were trained to use two abstract rules: “same” or “different”. They had to either hold or release a lever, depending on whether two successively presented pictures were the same or different, and depending on which rule was in effect. The rules and the behavioral responses were reflected most strongly and, on average, tended to be earlier in the PMC followed by the PFC and then the STR; few neurons in the ITC reflected the rules or the actions. By contrast, perceptual information (the identity of the pictures used as sample and test stimuli) was encoded more strongly and earlier in the ITC, followed by the PFC; they had weak, if any, effects on neural activity in the PMC and STR. These findings are discussed in the context of the anatomy and posited functions of these areas.

Monkeys have been introduced as model organisms to study neural correlates of numerical competence, but many of the behavioral characteristics of numerical judgments remain speculative. Thus, we analyzed the behavioral performance of two rhesus monkeys judging the numerosities 1 to 7 during a delayed match-to-sample task. The monkeys showed similar discrimination performance irrespective of the exact physical appearance of the stimuli, confirming that performance was based on numerical information. Performance declined smoothly with larger numerosities, and reached discrimination threshold at numerosity “4.” The nonverbal numerical representations in monkeys were based on analog magnitudes, object tracking process (“subitizing”) could not account for the findings because the continuum of small and large numbers shows a clear Weber fraction signature. The lack of additional scanning eye movements with increasing set sizes, together with indistinguishable neuronal response latencies for neurons with different preferred numerosities, argues for parallel encoding of numerical information. The slight but significant increase in reaction time with increasing numerosities can be explained by task difficulty and consequently time-consuming decision processes. The behavioral results are compared to single-cell recordings from the prefrontal cortex in the same subjects. Models for numerosity discrimination that may account for these results are discussed.