We examined whether prefrontal cortex (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, i.e., 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 monkey's 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.
prefrontal cortex; categorization; monkey; flexibility; goal directed; object vision
Items are categorized differently depending on the behavioral context. For instance, a lion can be an African animal or a type of cat. We recorded lateral prefrontal cortex (PFC) neural activity while monkeys switched between categorizing the same image set along two different category schemes with orthogonal boundaries. We found that each category scheme was largely represented by independent PFC neuronal populations and that activity reflecting a category distinction was weaker, but not absent, when that category was irrelevant. We suggest that the PFC represents competing category representations independently to reduce interference between them.
prefrontal cortex; categorization; monkey; flexibility; goal directed; object vision
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 (PFC) is important for flexible, context-dependent behavioral control. It also plays a critical role in short-term memory maintenance. Though many studies have investigated these functions independently, it is unclear how these two very different processes are realized by a single brain area. To address this we trained two monkeys on two variants of an object sequence memory task. These tasks had the same memory requirements but differed in how information was read out and used. For the “Recognition” task the monkeys had to remember two sequentially presented objects and then release a bar when a matching sequence was recognized. For the “Recall” task, the monkeys had to remember the same sequence of objects but were instead required to recall the sequence and reproduce it with saccadic eye movements when presented with an array of objects. After training we recorded the activity of PFC neurons during task performance. We recorded 222 neurons during the Recognition task, 177 neurons during the Recall task, and 248 neurons during the Switching task (interleaved blocks of Recognition and Recall). Task context had a profound influence on neural selectivity for objects. During the Recall task, the first object was encoded more strongly than the second object, while during the Recognition task the second object was encoded more strongly. In addition, most of the neurons encoded both the task and the objects, evidence for a single population responsible for these two critical prefrontal functions.
prefrontal; monkey; memory; sequence; multi-item; delay activity
Stokes et al demonstrate that cortical neurons that adapt their properties with task demands form patterns reflecting the shifting mental states needed to solve the task. Adaptive neurons may be critical to hallmarks of cognition: behavioral complexity and flexibility.
How are some thoughts favored over others? A wealth of data at the level of single neurons has yielded candidate brain areas and mechanisms for our best understood model: visual attention. Recent work has naturally evolved toward efforts at a more integrative, network, understanding. It suggests that focusing attention arises from interactions between widespread cortical and subcortical networks that may be regulated via their rhythmic synchronization.
Intelligent behavior requires acquiring and following rules. Rules define how our behavior should fit different situations. To understand its neural mechanisms, we simultaneously recorded from multiple electrodes in dorsolateral prefrontal cortex (PFC) while monkeys switched between two rules (respond to color versus orientation). We found evidence that oscillatory synchronization of local field potentials (LFPs) formed neural ensembles representing the rules: there were rule-specific increases in synchrony at “beta” (19–40 Hz) frequencies between electrodes. In addition, individual PFC neurons synchronized to the LFP ensemble corresponding to the current rule (color versus orientation). Furthermore, the ensemble encoding the behaviorally dominant orientation rule showed increased “alpha” (6–16 Hz) synchrony when preparing to apply the alternative (weaker) color rule. This suggests that beta-frequency synchrony selects the relevant rule ensemble, while alpha-frequency synchrony deselects a stronger, but currently irrelevant, ensemble. Synchrony may act to dynamically shape task-relevant neural ensembles out of larger, overlapping circuits.
This review examines the evidence for a neurobiological explanation of executive functions of working memory. We suggest that executive control stems from information about task rules acquired by mixed selective, adaptive coding, multifunctional neurons in the prefrontal cortex. The output of these neurons dynamically links the cortex-wide networks needed to complete the task. The linking may occur via synchronizing of neural rhythms, which may explain why we have a limited capacity for simultaneous thought.
basal ganglia; cognition; executive function; oscillation; prefrontal cortex; synchrony; working memory
Dopamine is thought to play a major role in learning. However, while dopamine D1 receptors (D1Rs) in the prefrontal cortex (PFC) have been shown to modulate working memory-related neural activity, their role in the cellular basis of learning is unknown. We recorded activity from multiple electrodes while injecting the D1R antagonist SCH23390 in the lateral PFC as monkeys learned visuomotor associations. Blocking D1Rs impaired learning of novel associations and decreased cognitive flexibility, but spared performance of already familiar associations. This suggests a greater role for prefrontal D1Rs in learning new, than performing familiar, associations. There was a corresponding greater decrease in neural selectivity and increase in alpha and beta oscillations in local field potentials for novel than familiar associations. Our results suggest that weak stimulation of D1Rs observed in aging and psychiatric disorders may impair learning and PFC function by reducing neural selectivity and exacerbating neural oscillations associated with inattention and cognitive deficits.
The ability to group items and events into functional categories is a fundamental characteristic of sophisticated thought. It is subserved by plasticity in many neural systems, including neocortical regions (sensory, prefrontal, parietal, and motor cortex), the medial temporal lobe, the basal ganglia, and midbrain dopaminergic systems. These systems interact during category learning. Corticostriatal loops may mediate recursive, bootstrapping interactions between fast reward-gated plasticity in the basal ganglia and slow reward-shaded plasticity in the cortex. This can provide a balance between acquisition of details of experiences and generalization across them. Interactions between the corticostriatal loops can integrate perceptual, response, and feedback-related aspects of the task and mediate the shift from novice to skilled performance. The basal ganglia and medial temporal lobe interact competitively or cooperatively, depending on the demands of the learning task.
classification; concept learning; memory systems
Neural correlates of visual categories have been previously identified in the prefrontal cortex (PFC). However, whether individual neurons can represent multiple categories is unknown. Varying degrees of generalization vs. specialization of neurons in the PFC have been theorized. We recorded from lateral PFC neural activity while monkeys switched between two different and independent categorical distinctions (Cats vs. Dogs, Sports Cars vs. Sedans). We found that many PFC neurons reflected both categorical distinctions. In fact, these multitasking neurons had the strongest category effects. This stands in contrast to our lab’s recent report that monkeys switching between competing categorical distinctions (applied to the same stimulus set) showed independent representations. We suggest that cognitive demands determine whether PFC neurons function as category “multi-taskers”.
Attention regulates the flood of sensory information into a manageable stream, and so understanding how attention is controlled is central to understanding cognition. Competing theories suggest visual search involves serial and/or parallel allocation of attention, but there is little direct, neural, evidence for either mechanism. Two monkeys were trained to covertly search an array for a target stimulus under visual search (endogenous) and pop-out (exogenous) conditions. Here we present neural evidence in the frontal eye fields (FEF) for serial, covert shifts of attention during search but not pop-out. Furthermore, attention shifts reflected in FEF spiking activity were correlated with 18–34 Hz oscillations in the local field potential, suggesting a ‘clocking’ signal. This provides direct neural evidence that primates can spontaneously adopt a serial search strategy and that these serial covert shifts of attention are directed by the FEF. It also suggests that neuron population oscillations may regulate the timing of cognitive processing.
Learning from experience requires knowing whether a past action resulted in a desired outcome. The prefrontal cortex and basal ganglia are thought to play key roles in such learning of arbitrary stimulus-response associations. Previous studies have found neural activity in these areas, similar to dopaminergic neuron signals that transiently reflect whether a response is correct or incorrect. However, it is unclear how this transient activity, which fades in under a second, influences actions that occur much later. Here we report sustained outcome-related responses in single neurons of both areas, which last for several seconds until the next trial. Moreover, the outcome on a single trial influences the neural activity and behavior on the next trial: behavioral responses are more often correct and single neurons more accurately discriminate between the possible responses when the previous trial was correct. These long-lasting signals about trial outcome provide a way to link one action to the next, and may allow reward signals to be combined over time to implement successful learning.
Volitional behavior relies on the brain’s ability to remap sensory flow to motor programs whenever demanded by a changed behavioral context. To investigate the circuit basis of such flexible behavior, we have developed a biophysically-based decision-making network model of spiking neurons for arbitrary sensorimotor mapping. The model quantitatively reproduces behavioral and prefrontal single-cell data from an experiment in which monkeys learn visuo-motor associations that are reversed unpredictably from time to time. We show that when synaptic modifications occur on multiple timescales, the model behavior becomes flexible only when needed: slow components of learning usually dominate the decision process. However, if behavioral contexts change frequently enough, fast components of plasticity take over, and the behavior exhibits a quick forget-and-learn pattern. This model prediction is confirmed by monkey data. Therefore, our work reveals a scenario for conditional associative learning that is distinct from instant switching between sets of well established sensorimotor associations.
Our thoughts have a limited bandwidth; we can only fully process a few items in mind simultaneously. To compensate, the brain developed attention, the ability to select information relevant to the current task, while filtering out the rest. Therefore, by understanding the neural mechanisms of attention we hope to understand a core component of cognition. Here, we review our recent investigations of the neural mechanisms underlying the control of visual attention in frontal and parietal cortex. This includes the observation that the neural mechanisms that shift attention were synchronized to 25 Hz oscillatory brain rhythms, with each shift in attention falling within a single cycle of the oscillation. We generalize these findings to present a hypothesis that cognition relies on neural mechanisms that operate in discrete, periodic computations, as reflected in ongoing oscillations. We discuss the advantages of the model, experimental support, and make several testable hypotheses.
attention; cognition; synchrony; oscillations
Complex goal-directed behaviors extend over time and thus depend on the ability to serially order memories and assemble compound, temporally coordinated movements. Theories of sequential processing range from simple associative chaining to hierarchical models in which order is encoded explicitly and separately from sequence components. To examine how short-term memory and planning for sequences might be coded, we used microstimulation to perturb neural activity in the supplementary eye field (SEF) while animals held a sequence of two cued locations in memory over a short delay. We found that stimulation affected the order in which animals saccaded to the locations, but not the memory for which locations were cued. These results imply that memory for sequential order can be dissociated from that of its components. Furthermore, stimulation of the SEF appeared to bias sequence endpoints to converge toward a location in contralateral space, suggesting that this area encodes sequences in terms of their endpoints rather than their individual components.
Errors induced by microstimulation of the supplementary eye fields while monkeys performed a remembered series of saccadic eye movements give insight into the nature of sequence coding in this cortical area.
The ability to generalize behaviour-guiding principles and concepts from experience is key to intelligent, goal-directed behaviour. It allows us to deal efficiently with a complex world and to adapt readily to novel situations. We review evidence that the prefrontal cortex-the cortical area that reaches its greatest elaboration in primates-plays a central part in acquiring and representing this information. The prefrontal cortex receives highly processed information from all major forebrain systems, and neurophysiological studies suggest that it synthesizes this into representations of learned task contingencies, concepts and task rules. In short, the prefrontal cortex seems to underlie our internal representations of the 'rules of the game'. This may provide the necessary foundation for the complex behaviour of primates, in whom this structure is most elaborate.