A neural mechanism for learning from delayed postingestive feedback.

Animals learn the value of foods based on their postingestive effects and thereby develop aversions to foods that are toxic1-6 and preferences to those that are nutritious7-14. However, it remains unclear how the brain is able to assign credit to flavors experienced during a meal with postingestive feedback signals that can arise after a substantial delay. Here, we reveal an unexpected role for postingestive reactivation of neural flavor representations in this temporal credit assignment process. To begin, we leverage the fact that mice learn to associate novel15-18, but not familiar, flavors with delayed gastric malaise signals to investigate how the brain represents flavors that support aversive postingestive learning. Surveying cellular resolution brainwide activation patterns reveals that a network of amygdala regions is unique in being preferentially activated by novel flavors across every stage of the learning process: the initial meal, delayed malaise, and memory retrieval. By combining high-density recordings in the amygdala with optogenetic stimulation of genetically defined hindbrain malaise cells, we find that postingestive malaise signals potently and specifically reactivate amygdalar novel flavor representations from a recent meal. The degree of malaise-driven reactivation of individual neurons predicts strengthening of flavor responses upon memory retrieval, leading to stabilization of the population-level representation of the recently consumed flavor. In contrast, meals without postingestive consequences degrade neural flavor representations as flavors become familiar and safe. Thus, our findings demonstrate that interoceptive reactivation of amygdalar flavor representations provides a neural mechanism to resolve the temporal credit assignment problem inherent to postingestive learning.

Case studies in neuroscience: reversible signatures of edema following electric and piezoelectric craniotomy drilling in macaques.

In vivo electrophysiology requires direct access to brain tissue, necessitating the development and refinement of surgical procedures and techniques that promote the health and well-being of animal subjects. Here, we report a series of findings noted on structural magnetic resonance imaging (MRI) scans in monkeys with MRI-compatible implants following small craniotomies that provide access for intracranial electrophysiology. We found distinct brain regions exhibiting hyperintensities in T2-weighted scans that were prominent underneath the sites at which craniotomies had been performed. We interpreted these hyperintensities as edema of the neural tissue and found that they were predominantly present following electric and piezoelectric drilling, but not when manual, hand-operated drills were used. Furthermore, the anomalies subsided within 2-3 wk following surgery. Our report highlights the utility of MRI-compatible implants that promote clinical examination of the animal's brain, sometimes revealing findings that may go unnoticed when incompatible implants are used. We show replicable differences in outcome when using electric versus mechanical devices, both ubiquitous in the field. If electric drills are used, our report cautions against electrophysiological recordings from tissue directly underneath the craniotomy for the first 2-3 wk following the procedure due to putative edema.NEW & NOTEWORTHY Close examination of structural MRI in eight nonhuman primates following craniotomy surgeries for intracranial electrophysiology highlights a prevalence of hyperintensities on T2-weighted scans following surgeries conducted using electric and piezoelectric drills, but not when using mechanical, hand-operated drills. We interpret these anomalies as edema of neural tissue that resolved 2-3 wk postsurgery. This finding is especially of interest as electrophysiological recordings from compromised tissue may directly influence the integrity of collected data immediately following surgery.

Competing rhythmic neural representations of orientations during concurrent attention to multiple orientation features.

When a feature is attended, all locations containing this feature are enhanced throughout the visual field. However, how the brain concurrently attends to multiple features remains unknown and cannot be easily deduced from classical attention theories. Here, we recorded human magnetoencephalography signals when subjects concurrently attended to two spatially overlapping orientations. A time-resolved multivariate inverted encoding model was employed to track the ongoing temporal courses of the neural representations of the attended orientations. We show that the two orientation representations alternate with each other and undergo a theta-band (~4 Hz) rhythmic fluctuation over time. Similar temporal profiles are also revealed in the orientation discrimination performance. Computational modeling suggests a tuning competition process between the two neuronal populations that are selectively tuned to one of the attended orientations. Taken together, our findings reveal for the first time a rhythm-based, time-multiplexing neural machinery underlying concurrent multi-feature attention.

[The cognitive neural mechanism of contour processing].

The core of visual processing is the identification and recognition of the objects relevant to cognitive behaviors. In natural environment, visual input is often comprised of highly complex 3-dimensional signals involving multiple visual objects. One critical determinant of object recognition is visual contour. Despite substantial insights on visual contour processing gained from previous findings, these studies have focused on limited aspects or particular stages of contour processing. So far, a systematic perspective of contour processing that comprehensively incorporates previous evidence is still missing. We therefore propose an integrated framework of the cognitive and neural mechanisms of contour processing, which involves three mutually interacting cognitive stages: contour detection, border ownership assignment and contour integration. For each stage, we provide an elaborated discussion of the neural properties, processing mechanism, and its functional interaction with the other stages by summarizing the relevant electrophysiological and human cognitive neuroscience evidence. Finally, we present the major challenges for further unraveling the mechanisms of visual contour processing.