Representation of foreseeable choice outcomes in orbitofrontal cortex triplet-wise interactions

Abstract

Shared neuronal variability has been shown to modulate cognitive processing. However, the relationship between shared variability and behavioral performance is heterogeneous and complex in frontal areas such as the orbitofrontal cortex (OFC). Mounting evidence shows that single-units in OFC encode a detailed cognitive map of task-space events, but the existence of a robust neuronal ensemble coding for the predictability of choice outcome is less established. Here, we hypothesize that the coding of foreseeable outcomes is potentially unclear from the analysis of units activity and their pairwise correlations. However, this code might be established more conclusively when higher-order neuronal interactions are mapped to the choice outcome. As a case study, we investigated the trial-to-trial shared variability of neuronal ensemble activity during a two-choice interval-discrimination task in rodent OFC, specifically designed such that a lose-switch strategy is optimal by repeating the rewarded stimulus in the upcoming trial. Results show that correlations among triplets are higher during correct choices with respect to incorrect ones, and that this is sustained during the entire trial. This effect is not observed for pairwise nor for higher than third-order correlations. This scenario is compatible with constellations of up to three interacting units assembled during trials in which the task is performed correctly. More interestingly, a state-space spanned by such constellations shows that only correct outcome states that can be successfully predicted are robust over 100 trials of the task, and thus they can be accurately decoded. However, both incorrect and unpredictable outcome representations were unstable and thus non-decodeable, due to spurious negative correlations. Our results suggest that predictability of successful outcomes, and hence the optimal behavioral strategy, can be mapped out in OFC ensemble states reliable over trials of the task, and revealed by sufficiency complex neuronal interactions.

Computational methods to study information processing in neural circuits

“…However, and somehow paradoxically, we observed that noise correlations were higher in trials where the animal made correct choices, suggesting that they may instead be useful for behavior [14] . Similar findings were also reported in other studies [99] , [79] .…”

“…These methods are based on the idea of correlating the sensory information in neural activity (rather than just neural activity regardless of its information) with the behavioral outcome on a trial-to-trial basis. For example, a way to establish the relevance of a given neural feature for behavior consists in evaluating whether higher values of information in the neural feature correlate on a trial-by-trial basis with more accurate behavioral performance [105] , [106] , [99] . A rigorous information-theoretic way to quantify this intuition has been developed [9] using the concept of Intersection Information, which involves information between three variables, i.e.…”

Distinct hippocampal-prefrontal neural assemblies coordinate memory encoding, maintenance, and recall

“… 20 , 37 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 Such dynamic coding means that the task features encoded by individual neurons can vary across sample, delay, and choice epochs 35 , 48 ; hence neurons not classically selective for individual task features may transiently contribute to short-term memory encoding and maintenance. 49 , 50 , 51 , 52 These observations highlight a coding regime that extends beyond straightforward mapping between behavior and the task-selective firing of individual units in PFC.…”

Uncovering hidden network architecture from spiking activities using an exact statistical input-output relation of neurons

“…Furthermore, it has been reported that the higher-order interactions encode stimulus information 43 , 51 (see also refs. 52 , 53 for a simulation study) and relate to animals cognitive functions such as expectation 54 , perceptual accuracy 55 , and prediction 56 . Thus, they potentially provide important clues on the architecture of cortical circuitries functioning in living systems.…”