The brain's memory function involves patterns of neural population spiking activity, shaped by experience and recurring over time. These neural population patterns are typically studied with respect to the three stages of acquisition, retention, and retrieval. Despite intensive investigation, the relationship between the features of population activity and the properties, computations, and codes for memory remains elusive. In this perspective, we synthesize recent advances in the study of memory from the viewpoint of brain network physiology, aiming for a comprehensive mapping between the properties and computations of memory and the features of population-activity codes. We propose that brain memory circuits implement trade-offs between conflicting demands on population codes. We anticipate that an important challenge for both discovery and translational neuroscience of memory is to study these trade-offs, delineating a safe zone in the population-activity space where neuronal circuits operate efficiently.
The mechanisms linking hypertension to cognitive decline remain unclear. Schaeffer et al. show that angiotensin II damages endothelium, oligodendrocyte precursors, and interneurons via AT1 signaling, independent of blood pressure. Targeting this pathway may protect the brain beyond pressure control alone.1.
Zhou et al.1 identify a C5aR1+ microglial subtype that amplifies neuroinflammation after traumatic brain injury and intracerebral hemorrhage. The mechanism reveals microglial-astrocyte-neutrophil crosstalk driving cerebral edema, highlighting C5aR1 as a therapeutic target and raising new questions about complement-glial interactions.
Value-guided decisions are a cornerstone of cognition, yet the underlying circuit-level mechanisms remain elusive. We used reinforcement learning to train recurrent neural network models endowed with Dale's law on a battery of economic choice tasks, which revealed a two-stage computational framework. First, value estimation occurs at the input level, where learned weights store subjective preferences and approximate the non-linear multiplication of reward magnitude and probability to yield expected values. This feedforward mechanism enables generalization to novel choice options. Second, option values are compared within the recurrent network, where specific connectivity patterns mediate robust winner-take-all decisions, with both excitatory and inhibitory neurons exhibiting value and choice selectivity. By training a single network on multiple tasks, we show compositional representations combining a shared computational schema with specialized neural modules. Reproducing key neurophysiological findings from the primate orbitofrontal cortex, our model unifies value computation, comparison, and generalization into a coherent framework with testable predictions.
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