Jonathan D. Wallis

Single neurons in prefrontal cortex encode abstract rules

The ability to abstract principles or rules from direct experience allows behaviour to extend beyond specific circumstances to general situations. For example, we learn the ‘rules’ for restaurant dining from specific experiences and can then apply them in new restaurants. The use of such rules is thought to depend on the prefrontal cortex (PFC) because its damage often results in difficulty in following rules. Here we explore its neural basis by recording from single neurons in the PFC of monkeys trained to use two abstract rules. They were required to indicate whether two successively presented pictures were the same or different depending on which rule was currently in effect. The monkeys performed this task with new pictures, thus showing that they had learned two general principles that could be applied to stimuli that they had not yet experienced. The most prevalent neuronal activity observed in the PFC reflected the coding of these abstract rules.

Neurons in the frontal lobe encode the value of multiple decision variables

A central question in behavioral science is how we select among choice alternatives to obtain consistently the most beneficial outcomes. Three variables are particularly important when making a decision: the potential payoff, the probability of success, and the cost in terms of time and effort. A key brain region in decision making is the frontal cortex as damage here impairs the ability to make optimal choices across a range of decision types. We simultaneously recorded the activity of multiple single neurons in the frontal cortex while subjects made choices involving the three aforementioned decision variables. This enabled us to contrast the relative contribution of the anterior cingulate cortex (ACC), the orbito-frontal cortex, and the lateral prefrontal cortex to the decision-making process. Neurons in all three areas encoded value relating to choices involving probability, payoff, or cost manipulations. However, the most significant signals were in the ACC, where neurons encoded multiplexed representations of the three different decision variables. This supports the notion that the ACC is an important component of the neural circuitry underlying optimal decision making.

Double dissociation of value computations in orbitofrontal and anterior cingulate neurons

Damage to prefrontal cortex (PFC) impairs decision-making, but the underlying value computations that might cause such impairments remain unclear. Here we report that value computations are doubly dissociable among PFC neurons. Although many PFC neurons encoded chosen value, they used opponent encoding schemes such that averaging the neuronal population extinguished value coding. However, a special population of neurons in anterior cingulate cortex (ACC), but not in orbitofrontal cortex (OFC), multiplexed chosen value across decision parameters using a unified encoding scheme and encoded reward prediction errors. In contrast, neurons in OFC, but not ACC, encoded chosen value relative to the recent history of choice values. Together, these results suggest complementary valuation processes across PFC areas: OFC neurons dynamically evaluate current choices relative to recent choice values, whereas ACC neurons encode choice predictions and prediction errors using a common valuation currency reflecting the integration of multiple decision parameters.

Oscillatory phase coupling coordinates anatomically dispersed functional cell assemblies

Hebb proposed that neuronal cell assemblies are critical for effective perception, cognition, and action. However, evidence for brain mechanisms that coordinate multiple coactive assemblies remains lacking. Neuronal oscillations have been suggested as one possible mechanism for cell assembly coordination. Prior studies have shown that spike timing depends upon local field potential (LFP) phase proximal to the cell body, but few studies have examined the dependence of spiking on distal LFP phases in other brain areas far from the neuron or the influence of LFP–LFP phase coupling between distal areas on spiking. We investigated these interactions by recording LFPs and single-unit activity using multiple microelectrode arrays in several brain areas and then used a unique probabilistic multivariate phase distribution to model the dependence of spike timing on the full pattern of proximal LFP phases, distal LFP phases, and LFP–LFP phase coupling between electrodes. Here we show that spiking activity in single neurons and neuronal ensembles depends on dynamic patterns of oscillatory phase coupling between multiple brain areas, in addition to the effects of proximal LFP phase. Neurons that prefer similar patterns of phase coupling exhibit similar changes in spike rates, whereas neurons with different preferences show divergent responses, providing a basic mechanism to bind different neurons together into coordinated cell assemblies. Surprisingly, phase-coupling–based rate correlations are independent of interneuron distance. Phase-coupling preferences correlate with behavior and neural function and remain stable over multiple days. These findings suggest that neuronal oscillations enable selective and dynamic control of distributed functional cell assemblies.

A Comparison of Abstract Rules in the Prefrontal Cortex, Premotor Cortex, Inferior Temporal Cortex, and Striatum

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.

Cross-species studies of orbitofrontal cortex and value-based decision-making

Recent work has emphasized the role that orbitofrontal cortex (OFC) has in value-based decision-making. However, it is also clear that a number of discrepancies have arisen when comparing the findings from animal models to those from humans. Here, we examine several possibilities that might explain these discrepancies, including anatomical difference between species, the behavioral tasks used to probe decision-making and the methodologies used to assess neural function. Understanding how these differences affect the interpretation of experimental results will help us to better integrate future results from animal models. This will enable us to fully realize the benefits of using multiple approaches to understand OFC function.

A hierarchy of intrinsic timescales across primate cortex

Specialization and hierarchy are organizing principles for primate cortex, yet there is little direct evidence for how cortical areas are specialized in the temporal domain. We measured timescales of intrinsic fluctuations in spiking activity across areas and found a hierarchical ordering, with sensory and prefrontal areas exhibiting shorter and longer timescales, respectively. On the basis of our findings, we suggest that intrinsic timescales reflect areal specialization for task-relevant computations over multiple temporal ranges.

Reversible large–scale modification of cortical networks during neuroprosthetic control

Brain-machine interfaces (BMIs) provide a framework for studying cortical dynamics and the neural correlates of learning. Neuroprosthetic control has been associated with tuning changes in specific neurons directly projecting to the BMI (hereafter referred to as direct neurons). However, little is known about the larger network dynamics. By monitoring ensembles of neurons that were either causally linked to BMI control or indirectly involved, we found that proficient neuroprosthetic control is associated with large-scale modifications to the cortical network in macaque monkeys. Specifically, there were changes in the preferred direction of both direct and indirect neurons. Notably, with learning, there was a relative decrease in the net modulation of indirect neural activity in comparison with direct activity. These widespread differential changes in the direct and indirect population activity were markedly stable from one day to the next and readily coexisted with the long-standing cortical network for upper limb control. Thus, the process of learning BMI control is associated with differential modification of neural populations based on their specific relation to movement control.

Evaluating choices by single neurons in the frontal lobe: outcome value encoded across multiple decision variables.

Damage to the frontal lobe can cause severe decision‐making impairments. A mechanism that may underlie this is that neurons in the frontal cortex encode many variables that contribute to the valuation of a choice, such as its costs, benefits and probability of success. However, optimal decision‐making requires that one considers these variables, not only when faced with the choice, but also when evaluating the outcome of the choice, in order to adapt future behaviour appropriately. To examine the role of the frontal cortex in encoding the value of different choice outcomes, we simultaneously recorded the activity of multiple single neurons in the anterior cingulate cortex (ACC), orbitofrontal cortex (OFC) and lateral prefrontal cortex (LPFC) while subjects evaluated the outcome of choices involving manipulations of probability, payoff and cost. Frontal neurons encoded many of the parameters that enabled the calculation of the value of these variables, including the onset and offset of reward and the amount of work performed, and often encoded the value of outcomes across multiple decision variables. In addition, many neurons encoded both the predicted outcome during the choice phase of the task as well as the experienced outcome in the outcome phase of the task. These patterns of selectivity were more prevalent in ACC relative to OFC and LPFC. These results support a role for the frontal cortex, principally ACC, in selecting between choice alternatives and evaluating the outcome of that selection thereby ensuring that choices are optimal and adaptive.

Heterogeneous reward signals in prefrontal cortex

Neurons encode upcoming rewards throughout frontal cortex. Recent papers have helped to determine that these signals play different roles in different frontal regions. Neurons in orbitofrontal cortex (PFo) appear to be responsible for calculating the specific value of an expected reward, information that can help efficiently guide decision-making. Similar signals are also present in the cingulate sulcus (PFcs). By contrast, reward signals in lateral prefrontal cortex (PFl) are consistent with a role in using reward to guide other cognitive processes, such as the allocation of attentional resources and using value information to guide learning other relationships in the environment such as arbitrary stimulus-response mappings. A remaining issue for future work is to specify the precise roles of PFo and PFcs. These two areas show very different patterns of connectivity with other brain areas, and it is currently unclear how this effects their contribution to decision-making.