Spatiotemporal Spike Coding of Behavioral Adaptation in the Dorsal Anterior Cingulate Cortex.

The frontal cortex controls behavioral adaptation in environments governed by complex rules. Many studies have established the relevance of firing rate modulation after informative events signaling whether and how to update the behavioral policy. However, whether the spatiotemporal features of these neuronal activities contribute to encoding imminent behavioral updates remains unclear. We investigated this issue in the dorsal anterior cingulate cortex (dACC) of monkeys while they adapted their behavior based on their memory of feedback from past choices. We analyzed spike trains of both single units and pairs of simultaneously recorded neurons using an algorithm that emulates different biologically plausible decoding circuits. This method permits the assessment of the performance of both spike-count and spike-timing sensitive decoders. In response to the feedback, single neurons emitted stereotypical spike trains whose temporal structure identified informative events with higher accuracy than mere spike count. The optimal decoding time scale was in the range of 70-200 ms, which is significantly shorter than the memory time scale required by the behavioral task. Importantly, the temporal spiking patterns of single units were predictive of the monkeys' behavioral response time. Furthermore, some features of these spiking patterns often varied between jointly recorded neurons. All together, our results suggest that dACC drives behavioral adaptation through complex spatiotemporal spike coding. They also indicate that downstream networks, which decode dACC feedback signals, are unlikely to act as mere neural integrators.

Reservoir Computing Properties of Neural Dynamics in Prefrontal Cortex.

Primates display a remarkable ability to adapt to novel situations. Determining what is most pertinent in these situations is not always possible based only on the current sensory inputs, and often also depends on recent inputs and behavioral outputs that contribute to internal states. Thus, one can ask how cortical dynamics generate representations of these complex situations. It has been observed that mixed selectivity in cortical neurons contributes to represent diverse situations defined by a combination of the current stimuli, and that mixed selectivity is readily obtained in randomly connected recurrent networks. In this context, these reservoir networks reproduce the highly recurrent nature of local cortical connectivity. Recombining present and past inputs, random recurrent networks from the reservoir computing framework generate mixed selectivity which provides pre-coded representations of an essentially universal set of contexts. These representations can then be selectively amplified through learning to solve the task at hand. We thus explored their representational power and dynamical properties after training a reservoir to perform a complex cognitive task initially developed for monkeys. The reservoir model inherently displayed a dynamic form of mixed selectivity, key to the representation of the behavioral context over time. The pre-coded representation of context was amplified by training a feedback neuron to explicitly represent this context, thereby reproducing the effect of learning and allowing the model to perform more robustly. This second version of the model demonstrates how a hybrid dynamical regime combining spatio-temporal processing of reservoirs, and input driven attracting dynamics generated by the feedback neuron, can be used to solve a complex cognitive task. We compared reservoir activity to neural activity of dorsal anterior cingulate cortex of monkeys which revealed similar network dynamics. We argue that reservoir computing is a pertinent framework to model local cortical dynamics and their contribution to higher cognitive function.

Behavioral Regulation and the Modulation of Information Coding in the Lateral Prefrontal and Cingulate Cortex.

To explain the high level of flexibility in primate decision-making, theoretical models often invoke reinforcement-based mechanisms, performance monitoring functions, and core neural features within frontal cortical regions. However, the underlying biological mechanisms remain unknown. In recent models, part of the regulation of behavioral control is based on meta-learning principles, for example, driving exploratory actions by varying a meta-parameter, the inverse temperature, which regulates the contrast between competing action probabilities. Here we investigate how complementary processes between lateral prefrontal cortex (LPFC) and dorsal anterior cingulate cortex (dACC) implement decision regulation during exploratory and exploitative behaviors. Model-based analyses of unit activity recorded in these 2 areas in monkeys first revealed that adaptation of the decision function is reflected in a covariation between LPFC neural activity and the control level estimated from the animal's behavior. Second, dACC more prominently encoded a reflection of outcome uncertainty useful for control regulation based on task monitoring. Model-based analyses also revealed higher information integration before feedback in LPFC, and after feedback in dACC. Overall the data support a role of dACC in integrating reinforcement-based information to regulate decision functions in LPFC. Our results thus provide biological evidence on how prefrontal cortical subregions may cooperate to regulate decision-making.

Behavioral shifts and action valuation in the anterior cingulate cortex.

Rapid optimization of behavior requires decisions about when to explore and when to exploit discovered resources. The mechanisms that lead to fast adaptations and their interaction with action valuation are a central issue. We show here that the anterior cingulate cortex (ACC) encodes multiple feedbacks devoted to exploration and its immediate termination. In a task that alternates exploration and exploitation periods, the ACC monitored negative and positive outcomes relevant for different adaptations. In particular, it produced signals specific of the first reward, i.e., the end of exploration. Those signals disappeared in exploitation periods but immediately transferred to the initiation of trials-a transfer comparable to learning phenomena observed for dopaminergic neurons. Importantly, these were also observed for high gamma oscillations of local field potentials shown to correlate with brain imaging signal. Thus, mechanisms of action valuation and monitoring of events/actions are combined for rapid behavioral regulation.

Coordination of high gamma activity in anterior cingulate and lateral prefrontal cortical areas during adaptation.

The anterior cingulate cortex (ACC) and the lateral prefrontal cortex (LPFC) process complementary information for planning and evaluating behavior. This suggests at least that processes in these two areas are coordinated during behavioral adaptation. We analyzed local field potentials recorded in both regions in two monkeys performing a problem-solving task that alternated exploration and repetitive behaviors with the specific prediction that neural activity should reveal interareal coordination mainly during exploration. Both areas showed increased high gamma power after errors in exploration and after rewards in exploitation. We found that high gamma (60-140 Hz) power increases in ACC were followed by a later increase in LPFC only after negative feedback (errors) or first positive feedback (correct) during the exploration period. The difference in latencies between the two structures disappeared in repetition period. Simultaneous recordings revealed correlations between high gamma power in the two areas around feedback; however, correlations were observed in both exploration and repetition. In contrast, postfeedback beta (10-20 Hz) power in ACC and LPFC correlated more frequently during repetition. Together, our data suggest that the coordination between ACC and LPFC activity is expressed during adaptive as well as stable behavioral periods but with different modes depending on the behavioral period.

Anatomy of hierarchy: feedforward and feedback pathways in macaque visual cortex.

The laminar location of the cell bodies and terminals of interareal connections determines the hierarchical structural organization of the cortex and has been intensively studied. However, we still have only a rudimentary understanding of the connectional principles of feedforward (FF) and feedback (FB) pathways. Quantitative analysis of retrograde tracers was used to extend the notion that the laminar distribution of neurons interconnecting visual areas provides an index of hierarchical distance (percentage of supragranular labeled neurons [SLN]). We show that: 1) SLN values constrain models of cortical hierarchy, revealing previously unsuspected areal relations; 2) SLN reflects the operation of a combinatorial distance rule acting differentially on sets of connections between areas; 3) Supragranular layers contain highly segregated bottom-up and top-down streams, both of which exhibit point-to-point connectivity. This contrasts with the infragranular layers, which contain diffuse bottom-up and top-down streams; 4) Cell filling of the parent neurons of FF and FB pathways provides further evidence of compartmentalization; 5) FF pathways have higher weights, cross fewer hierarchical levels, and are less numerous than FB pathways. Taken together, the present results suggest that cortical hierarchies are built from supra- and infragranular counterstreams. This compartmentalized dual counterstream organization allows point-to-point connectivity in both bottom-up and top-down directions.

Expectations, gains, and losses in the anterior cingulate cortex.

The anterior cingulate cortex (ACC) participates in evaluating actions and outcomes. Little is known on how action-reward values are processed in ACC and if the context in which actions are performed influences this processing. In the present article, we report ACC unit activity of monkeys performing two tasks. The first task tested whether the encoding of reward values is co ntext dependent-that is, dependent on the size of theother rewards that are available in the current block of trials. The second task tested whether unexpected events signaling a change in reward are represented. We show that the context created by a block design (i.e., the context of possible alternative rewards) influences the encoding of reward values, even if no decision or choice is required. ACC activity encodes the relative and not absolute expected reward values. Moreover, cingulate activitysignals and evaluates when reward expectations are violated by unexpected stimuli, indicating reward gains or losses.