How do new brain regions evolve? The cerebellar nuclei, the output structures of the cerebellum, are an ideal model for studying this question due to their striking diversification across vertebrates: Mammals have three bilaterally symmetric nuclei, birds and reptiles have two, and amphibians and cartilaginous fishes have one. Here, we review comparative, developmental, and transcriptomic data on the evolution of the cerebellar nuclei. We propose that the repeated duplication of a conserved cell type set coupled with excitatory neuron divergence underlies cerebellar nucleus diversification. We also discuss the possible origins of the ancestral cerebellar nucleus, likely through a duplication-and-divergence event of extracerebellar hindbrain structures. Finally, we explore how similar duplication-and-divergence mechanisms operate in other brain regions, underscoring this mechanism as a general principle governing the emergence of new brain structures.

Our understanding of hypothalamic circuits that control social and other innate survival behaviors has exploded in recent years, thanks to the application of powerful genetically based tools for marking, mapping, manipulating, and monitoring specific neuronal subpopulations. Yet there remains an important set of unanswered questions that must be addressed to fully understand this complex system, including how the hypothalamus encodes affective internal states underlying these behaviors. In systems neuroscience, neural representations of cognitive variables have been revealed by analyzing the activity and dynamics of neural manifolds hidden within high-dimensional population neural activity. These signals are not detectable in the activity of individual neurons or neuron types. Because the hypothalamus has traditionally been viewed as the least "cognitive" part of the brain, the so-called "population doctrine" in cognitive neuroscience has not been widely applied to this system. Here, we discuss recent data that suggest that the application of this manifold-level approach may be useful for identifying representations and mechanisms of internal affective state coding in the hypothalamus. In turn, these findings argue that cognition and emotion may share computational mechanisms to a greater degree than previously assumed.

Homeothermy, the physiologic capacity to maintain a constant core body temperature, provided a critical survival advantage for mammalian and avian phyla, enabling the colonization of diverse habitats. However, the higher metabolic demands associated with homeothermy necessitate greater food intake. When challenged by food deprivation or harsh environmental conditions, many mammalian and avian species initiate adaptive energy-conserving survival strategies-including hibernation and daily torpor-during which their body temperature decreases far below its homeostatic range. Despite their biological significance and potential biomedical applications, the neural circuit mechanisms regulating entry into these hypometabolic states and their relationship to characterized thermoregulatory pathways remain to be fully elucidated. Here we review known thermoregulatory mechanisms and recent findings concerning the neural control of torpor and hibernation to identify points of convergence as well as important next steps in pursuit of an understanding of these regulatory pathways.

It is unusual for cognitive neuroscientists to reach consensus around which computations a brain region implements, what algorithms are used to achieve those computations and how the algorithms are implemented by cells and circuits (i.e., Marr's three levels of analysis). Over the past decade, there has been notable progress in delineating such a unified functional understanding in the Drosophila central complex. In this review, we summarize how the central complex operates as a computational device that calculates the values of angles and two-dimensional vectors important for navigational behavior. Appreciating how these insights were made in Drosophila suggests a roadmap for reaching a similar level of understanding in larger brains.

Perhaps most well-known for its penetrant role in synucleinopathies, alpha-synuclein's nonpathological function remains incompletely characterized. Most widely regarded as a putative presynaptic protein, a growing body of work over the last few decades demonstrates that alpha-synuclein participates in a broad and more diverse set of neuronal functions. Alpha-synuclein is a small, intrinsically disordered protein comprising three functionally distinct regions that mediate membrane binding, vesicle clustering, and protein-protein interactions. At synapses, alpha-synuclein participates in multiple steps of neurotransmission, including organization of the vesicle reserve pool, recruitment of release machinery, and recycling of synaptic vesicles. Emerging evidence further supports roles for alpha-synuclein beyond classical presynaptic compartments, including interactions with nonsynaptic membranes and secretion via extracellular vesicles. This review aims to integrate the literature on alpha-synuclein's structure and function to better underscore how these properties may contribute to vulnerability in disease when these normal functions are lost.

The lower brainstem is a key hub linking visceral physiology with the regulation of brain states. This review synthesizes recent findings demonstrating how several nuclei within this region-including the nucleus of the solitary tract (NST), parabrachial nucleus (PBN), and other medullary circuits-function as an integrated network that couples sleep-wake regulation to the body's homeostatic demands. The NST serves as a central gateway, translating cardiovascular, immune, and digestive signals into a sleep drive, whereas the PBN plays a pivotal role in processing threat-related inputs to promote arousal. Several populations of GABAergic neurons in the medulla induce both motor suppression and sleep. In addition, cholinergic neurons in the nucleus ambiguus and catecholaminergic cells in the ventrolateral medulla and locus coeruleus regulate sleep-wake states together with somatic and autonomic motor activity. Collectively, these findings establish the brainstem not merely as a collection of reflex centers but as a master coordinator aligning global brain states with peripheral bodily functions.

The neuroscience of planning has long been analogized to search algorithms in artificial intelligence (AI), which simulate future actions to guide immediate choices. We argue that advances in both neuroscience and AI suggest that planning is better understood to encompass a broader class of computations where mental simulation supports learning, often well before a decision is needed. We review three neurocomputational mechanisms that illustrate this shift. First, hippocampal replay resembles search but also often occurs prospectively or offline, likely training downstream circuits rather than directly guiding choice. Second, temporally abstract representations, such as grid cells, can enable planning without iterative search. Third, metalearning may shape how prefrontal dynamics implement task-specific planning strategies, echoing how AI systems learn to adapt across contexts. This view recasts the brain's planning machinery as a family of learning processes that leverage simulations to build representations and strategies, with forward search as one special case.

Psychedelics are a broad category of compounds that induce altered states of consciousness. These drugs have shown remarkable promise for the treatment of debilitating disorders ranging from posttraumatic stress disorder to depression and addiction. Although early studies focused on linking binding targets of psychedelics to their therapeutic effects, these pharmacological and biochemical explanations fail to account for the diversity, durability, and context dependence of psychedelics' clinical and acute subjective effects. More recently, neurobiological explanations offer fresh insights and demonstrate that a unifying property of psychedelics is that these compounds reopen critical periods, induce metaplasticity, and reorganize the extracellular matrix. Here we review this evidence and argue that the neurobiological and therapeutic effects of psychedelics challenge the biochemical imbalance model that has dominated translational neuroscience since the 1950s and favor instead a learning model that better accounts for psychedelics' unique therapeutic profile.

Predictive coding proposes that the brain constructs internal models of the world to continuously predict sensory input and uses resulting errors to refine these models. Over the past several decades, neurophysiological studies have reported activity patterns consistent with this view. However, varied definitions and inconsistent empirical evidence have raised questions about its validity and explanatory scope. In this review, we provide a historical overview of the predictive processing framework and evaluate its empirical support, particularly focusing on sensory prediction error signals. We argue that clarifying what information these signals represent is crucial, as they may appear similar in their responses yet reflect fundamentally different underlying computations. We then revisit predictive coding, highlighting alternative accounts of how sensory prediction error signals encode information. Finally, we outline key directions for future work, aiming to provide a constructive roadmap for the next phase of predictive processing research and to advance our understanding of the neuronal algorithms underlying perception and cognition.

Path integration (PI), the ability to keep track of position and orientation from self-motion, is a sensitive cognitive marker of Alzheimer's disease. While entorhinal grid cells are central to PI, we focus here on the broader functional circuit supporting PI and the impact of Alzheimer's disease within it. This circuit includes orientation from head direction cells, landmark-based error correction, and signals encoding current or intended movement direction, which we suggest may rely on theta-modulated directional cells and theta sweeps in grid and place cell firing. The early vulnerability of PI, particularly angular PI, may reflect multiple sources: pathology in the anterodorsal thalamus degrading head direction coding; disrupted theta rhythmicity and thus theta-modulated directional signals, potentially reflecting cholinergic dysfunction; and retrosplenial landmark-resetting failures allowing angular drift. We advocate for further cross-species investigation of PI tasks with electrophysiological measures to fully identify the underlying circuit mechanisms and their impairment in Alzheimer's disease.

The thermosensory system enables animals to detect and respond to changes in external temperature and is therefore essential for survival, yet remains significantly understudied. This review summarizes current knowledge of its organization in adult Drosophila melanogaster: from peripheral cellular receptors and molecular detection mechanisms to the brain circuits that process thermal information, beginning with second-order thermosensory projection neurons and their targets. The powerful tools available in Drosophila have driven significant advances, revealing the organization of this system at the periphery, the reach of thermosensory pathways within the brain, and the range of behaviors directly influenced by external temperature. These findings also open new avenues to examine how the thermosensory system has been reshaped under changing thermal conditions as Drosophila species evolved to colonize diverse thermal environments.

Category learning-the ability to group individual objects, experiences, and concepts into higher-level abstract representations-is fundamental to fast, flexible decision-making and generalization of knowledge to novel situations. While categories and concepts have traditionally been studied in humans and nonhuman primates, this review puts the focus on the lesser-known domain of rodent category learning. Within the context of human and nonhuman primate work, we highlight the behavioral capabilities and limitations of rodents in categorization tasks, and discuss the neural circuits identified so far. Finally, we outline a roadmap for uncovering the systems and synaptic mechanisms that support the representation of learned categories in the mammalian brain.

Posttraumatic stress disorder (PTSD) is unique in its requirement of an external stressor for the development of disease, leading to dysregulated mood, intrusive memories, and avoidance symptoms. Over the past decade, insights from human neuroimaging, mechanistic neural circuitry studies in animal models, and genomic work have revolutionized our understanding of this psychiatric illness, with emerging data from large-scale PTSD genome-wide association studies providing novel insights into the mechanism of PTSD. Despite these advances, therapeutic interventions remain limited, and psychotherapy remains the first-line treatment over pharmacological interventions. Here, we review the basic epidemiology of PTSD with an overview of common types of trauma, neurocircuits and molecular mechanisms contributing to fear learning, genomics studies, and current and emerging treatments. Although PTSD remains highly prevalent, recent advances and emerging identification of neural circuits and putative molecular targets offer an exciting opportunity to advance the treatment of this debilitating condition.

During pregnancy, the maternal body undergoes profound, coordinated physiological adaptations to support the developing fetus, including major shifts in immune regulation and dramatic changes in the vascular system. Accompanying these peripheral adaptations, recent longitudinal studies in humans point to significant remodeling of the nervous system, occurring in lockstep with increases in gonadal hormone production. To understand the neural adaptations tied to pregnancy and the postpartum period, a holistic approach is essential-one that accounts for changes across multiple peripheral systems. In this review, we consider the impact of the endocrine, cardiovascular, microbiome, and immune systems on the maternal brain. By adopting this integrative approach, we aim to better understand the biological pathways that shape the maternal brain during normative pregnancies and those marked by adverse events.

The head serves as a multifunctional nexus through which animals engage with the external world-gathering sensory input, breathing, feeding, and producing communication signals. These diverse functions must be executed with constrained anatomical resources, often requiring the same oropharyngeal structures to participate in multiple behaviors. Such overlap demands precise coordination to prevent catastrophic errors such as choking or aspiration, while ensuring that breathing and eating proceed without conflict. Operating largely outside conscious awareness, brainstem circuits choreograph these actions by controlling multiple muscle groups with remarkable precision and adaptability. Interactions between different rhythm-generating circuits can weave distinct actions-like whisking with sniffing or licking and chewing-into integrated behaviors that serve survival needs. Recent advances in circuit dissection, targeted manipulations, and large-scale electrophysiological recordings are uncovering neurons and functional connections that support precise, coordinated, yet flexible, orofacial actions. These studies provide new insights into how brainstem circuitry achieves precise and effective control over one of biology's most demanding motor challenges.