Perception depends on the brain's ability to transform high-dimensional sensory inputs into low-dimensional internal models that support adaptive behavior. Evidence supports two frameworks for sensory perception-representational processing, in which stimulus features are progressively integrated into complex perceptual objects across a cortical hierarchy, and predictive processing, in which internally generated predictions are continuously reconciled with incoming sensory signals. Yet how these frameworks are mechanistically implemented in neural circuits, and whether they can be unified, remains an open question. Here, we review recent studies in mouse primary sensory and higher-order association cortex demonstrating that cell-type-specific transcriptional programs may provide a critical mechanistic link between these frameworks and circuit functions. In primary sensory cortices, neurons that function as stable feature detectors or respond to sensory prediction errors correspond to distinct molecularly defined cell types. In higher-order association cortices, distinct inhibitory cell-type compositions and plasticity-related gene expression support both associative learning for representational processing and error learning for predictive processing. We discuss how cell-type-specific transcriptional programs may endow cell types and circuits with the capacity to support both representational and predictive processing modes in a behavioral state-dependent manner. This could potentially enable active sensation during behavioral engagement as well as memory consolidation and model updating during behavioral quiescence. Together, these studies suggest that examining how gene expression programs equip specific cell types with relevant computational properties is a promising approach that can integrate these frameworks and provide a new understanding of how sensory perception is implemented in the brain.
Parkinson's disease (PD) is an age-associated movement disorder with many variable symptoms, albeit with no treatments to slow or halt clinical progression. Its etiology is multifactorial with a genetic heritability of ∼27%, and even monogenic disease in families manifests with incomplete/reduced penetrance and variable expressivity. Over the past 28 years, genetic linkage studies have identified causal mutations to inform clinical diagnosis, modeling, and therapeutic development. Indeed, clinical trials to lower alpha-synuclein (SNCA) expression or inhibit leucine-rich repeat kinase 2 (LRRK2) activity are far advanced. Evolutionarily, the population frequencies of several Mendelian discoveries have been driven by positive selection as they provide an advantage in immune defense. Their precise molecular deficits converge about synaptic, mitophagy/autophagic, and immune processes, while physiologic modeling highlights peripheral inflammation as a driver of dopaminergic neuronal loss leading to motor dysfunction. In addition, genome-wide association studies have identified a large number of loci and genetic variants. Nevertheless, these require much larger sample sizes to see ever diminutive effects, as predicted by Fisher's infinitesimal model. Most of the heritability of PD is not explained by single-nucleotide polymorphisms, and few of these associated variants are biologically informative as their effect sizes are too small and pleiotropic. Despite technologic advances to enable global genome sequencing and rare-variant discovery, association is not causation. Rather the discovery of new genes and pathogenic variants that cause PD requires a family-based approach. This is best accomplished with 1) singleton patients with young-onset PD and their asymptomatic first-degree relatives and 2) comparative analysis of the genomes of affected individuals in multi-incident pedigrees. Complimentary investments in longitudinal family-based studies that extend beyond movement disorders are needed to inform disease prognosis, enable biomarker discovery and validation, and enable clinical trials.
Parental care in animals consists of an incredible diversity of forms, providing powerful opportunities for studying the proximate basis of behavior. Three-spined stickleback fish, a classic model system in ecology, evolution, and behavior, are good subjects for studying the neurobiological basis of paternal care as male sticklebacks are solely responsible for providing care to their developing offspring. Here we review what is known about the neural, neuroendocrine, genetic, and molecular basis of paternal care in sticklebacks, highlighting the ways in which natural phenotypic variation within and among populations has been leveraged to improve our understanding of the proximate basis of behavioral variation. For example, diversity in care among populations, including the complete evolutionary loss of parental care in the so-called "white" sticklebacks, provides insight into the proximate mechanisms by which highly divergent behavior in whites diverged so rapidly and dramatically from the ancestral care-giving form. Moreover, comparisons within populations across stages of care reveal deeply conserved neural, genomic, and neuroendocrine mechanisms regulating paternal and maternal behavior across vertebrates. Moving forward, improved neuroscientific tools and resources for sticklebacks will enable functional manipulations and in vivo studies, opening up novel opportunities to address outstanding questions about the origin of behavioral diversity.
The past decade of multi-omics studies revealed perturbations in genetic, epigenetic, transcriptomic, proteomic, and metabolic networks in Alzheimer's disease (AD) detected in brain, cerebrospinal fluid (CSF), and blood biospecimens. Interactions among these networks and environmental factors are thought to contribute to risk and progression of this neurodegenerative dementia. Understanding the molecular and environmental risk in AD across all populations is essential in the development of cures and biomarkers for this complex disease. While most molecular studies to date have focused on populations of European ancestry, emerging multi-ancestry and multi-omics studies are revealing both shared and ancestry-specific biological signatures associated with disease susceptibility, biomarker profiles, and clinical presentation. Genomic studies show that established AD risk loci such as APOE, ABCA7, and TREM2 exhibit ancestry-dependent effects, while trans-ethnic genome-wide association studies identified novel disease risk loci (e.g., LRRC4C, LHX5-AS1) and protective haplotypes unique to African American (AA) and admixed populations. Epigenomic and transcriptomic studies reveal ancestry-linked variation in chromatin accessibility, DNA methylation, and gene expression, particularly in immune, lipid metabolism, and synaptic pathways. Proteomic analyses demonstrate differences in CSF and brain protein networks, including extracellular matrix and synaptic modules enriched or reduced in AA AD brains. Metabolomic and lipidomic data further highlight differential abundance in non-European cohorts. Integrating these multi-omics layers across ancestries provides a framework for understanding how genetic background and environmental context interact to drive AD heterogeneity. Such integrative, ancestry-aware approaches will refine biomarker interpretation, improve diagnostic accuracy, and guide development of therapeutics for AD.
Mosquitoes use their sense of smell in a variety of behaviors, such as searching for nectar, host-seeking, and finding oviposition sites. These behaviors, many of which are sexually dimorphic, require modulation in response to the internal state and the external environment of the animal. For example, a female mosquito is attracted to human odors, but after blood-feeding, its attraction to human hosts reduces and the attraction to oviposition sites eventually increases. Such changes in the internal state and their effects on the sensory systems are implemented in the insect brain by an intricate choreography involving many neuromodulators in a sex-specific manner. With a focus on the mosquito olfactory system, we review the expression profiles and the functional roles of monoamine neuromodulators (dopamine, serotonin, octopamine and tyramine) and some of the major neuropeptides, including FMRFamide, SIFamide, short neuropeptide F, allatostatin A, myoinhibitory peptide, allatotropin, and tachykinin-related peptide.
Understanding how decision-making changes across the lifespan is a central challenge for neuroscience, yet research on cognitive aging remains largely disconnected from the theoretical and computational advances shaping modern systems neuroscience. Over recent decades, theoretical frameworks have transformed how we study cognition in young, healthy brains. In contrast, aging research has relied on single-metric behavioral measures, cross-sectional comparisons, and descriptive neural analyses, limiting our ability to explain fundamental differences in individual aging trajectories. This gap represents a missed opportunity: aging offers a powerful platform for testing theories of neural computation, stability, and flexibility under changing biological constraints. We argue that closer integration between aging research and contemporary theoretical neuroscience will move the field from descriptive towards mechanistic insights. Here, we outline how recent advances in behavioral quantification, latent-state modeling, dynamical systems, encoding models, representational geometry, and recurrent neural networks offer a rich theoretical toolkit for studying decision-making across the lifespan.
Parkinson's disease (PD) is a multifactorial and progressive neurodegenerative disorder characterized by a complex symptomatology, including a broad spectrum of motor and non-motor symptoms. Disease modeling in animals represents a keystone part of the research aimed at understanding molecular bases driving PD onset, and specific mechanisms shaping pathological trajectories along disease progression, sustaining the inter-individual variability in PD manifestation. Multiple experimental approaches using toxins, viral vectors, and genetic perturbations have been developed to reproduce key PD features in animals, resulting in a wide variety of models. However, a minority of models show multiple aspects of PD-related phenomenology. In this short review, we provide a synthetic description of selected rodent PD models, including those based on stronger etiological construct validity as well as newly developed models, and discuss their strengths and limitations in reproducing PD features. The main intention is to provide a simple resource for selecting optimal PD models that show multiple histopathological and behavioral alterations, thus more closely mirroring clinical PD complexity.
Brain disorders exhibit profound spatiotemporal complexity, involving intricate disruptions in synaptic transmission and neurochemical signaling. A precise understanding of these dynamic processes is crucial for developing effective therapeutics but remains technically challenging. Genetically encoded fluorescent sensors have emerged as transformative tools to bridge this gap by enabling the real-time monitoring of neurochemical events with high specificity. This review highlights how cutting-edge single-fluorophore biosensors for neurochemicals are being applied to dissect the pathological mechanisms underlying diverse brain disorders. We further outline future frontiers in sensor technology, including data-driven machine learning-guided design, spectral multiplexing, and quantitative fluorescence lifetime imaging. Together, these advances provide a robust and evolving toolkit for elucidating the neurochemical basis of brain disease and guiding next-generation therapeutic strategies.
The African spiny mouse (Acomys dimidiatus) is a unique mammalian model capable of scarless tissue regeneration, extending to the nervous system. Unlike conventional rodents, Acomys show significantly higher levels of adult brain stem cells, enhanced functional plasticity after brain injury, and the ability to regenerate and regain function following severe spinal cord damage. While the regenerative capacity of the Acomys central nervous system (CNS) is only beginning to be explored, existing studies have already challenged the long-standing dogma that adult mammals are incapable of CNS recovery after injury. This review provides a critical overview on the current knowledge of Acomys nervous system biology, from development to repair. We summarize the known cellular and mechanistic insights and highlight the current outstanding questions and research priorities. Understanding how Acomys achieves CNS functional recovery, an ability unmatched by any other known mammal, may ultimately guide strategies to enhance repair in nonregenerative mammals, including humans.
Parental care enhances offspring survival but requires profound alteration of parental physiological and behavioural states. Among vertebrates, teleost fishes exhibit great diversity in parental strategies, providing opportunities to investigate how the brains of different species integrate internal and external cues to produce adaptive care behaviours. Key regulators of parenting include prolactin, vasopressin, oxytocin and gonadal steroids, showing that endocrine signals coordinate parental motivation, protection, and feeding behaviour. At the neural level, recent studies highlight the hypothalamus as central for the integration of reproductive and energetic states. Key regulators of the balance between offspring-directed behaviour and self-maintenance include neurons that express galanin, mirroring conserved motifs described in mammals. As emerging tools advance our understanding of the mechanisms that underlie fish parental care, we will gain deeper insights into the evolution of neural circuits for social behaviour.
Across diverse animals, sex differentiation generates distinct patterns of innate social behaviors. The neural circuits underlying these behaviors are 'hard-wired', with neuronal development programs ensuring the same circuit wiring across individuals. Aspects of their development are modified by effectors of sex determination. These factors, including Fruitless in Drosophila melanogaster and Estrogen Receptor α in mammals, shape sex-specific behaviors by altering neuron numbers, axon and dendrite anatomy, synaptic connectivity, and neuronal physiology to allow for distinct neural connections and behavioral repertoires. While the sex-sensitive transcription factors were identified long ago, the transcriptional mechanisms through which they affect brain development are only recently coming into focus. Defining the molecular mechanisms by which sex-sensitive transcription factors act to alter the brain and behavior has profound implications for 1) how genomic regulatory elements pattern complex behavior, 2) the critical developmental windows in which neurons and brain regions can be altered in pleiotropic ways, and 3) the discrete developmental events underlying circuit assembly and how those events are susceptible to disruption. Here, we review how the Fruitless transcription factor acts in context-specific ways to induce sex differences in circuit organization in Diptera.
The host seeking and oviposition behavior of female mosquitoes makes important contributions to the spread of devastating mosquito-borne diseases. Among the sensory cues mosquitoes exploit to perform these behaviors, the physical cues of heat and humidity serve as key indicators of target presence and proximity. Recent evidence suggests that mosquitoes initially sense heat from a human host in the form of infrared radiation, which they find attractive when combined with other host-associated cues. Infrared detection relies on the cation channel TRPA1, its function potentiated by two opsins. Closer in, mosquito heat seeking is also driven by heat transmitted by convection and conduction. Ionotropic receptor family members have key roles in these processes: Ir21a and Ir93a are required by cooling-activated thermosensors, which are major drivers of heat seeking, while the broadly expressed coreceptor Ir25a is absolutely required for heat seeking. Humans also produce short-range humidity gradients, which appear to act redundantly with heat to drive host seeking. Ionotropic receptors also mediate humidity sensing (hygrosensation): Ir40a supports dry-activated hygrosensors (dry cells), Ir68a supports moist-activated hygrosensors (moist cells) and Ir93a supports both. Dry cells and moist cells function in a redundant manner to promote blood feeding, while moist cells are key drivers of oviposition site seeking. Both Ir68a and Ir93a mutants are unable to find water to lay eggs. Despite a growing parts list of sensory receptor molecules and neurons driving host seeking and oviposition, understanding the mechanisms by which these cells and molecules detect sensory stimuli remains a major challenge.
The early decades of neuroscience drew inspiration from the rich diversity of animal life, but in recent years, the field has converged on a narrow set of canonical models. These organisms provide access to a wealth of scientific tools, a large community, and the ability to rapidly build on existing knowledge, but they represent only a small sample of the neural architectures and behaviors found in the animal kingdom. Here, we highlight the value of harnessing a more diverse panel of organisms to study the neural basis of behavior using the sensory systems as an example. From moths navigating using the Milky Way, to deep-sea dragonfish detecting far-red bioluminescence via a chlorophyll-derived photosensitizer, and octopuses tasting by touch through chemoreceptors in their suckers, studies of diverse systems reveal novel mechanisms for solving sensory challenges, as well as striking examples of convergence. Developments in species-agnostic scientific tools like behavioral tracking, gene editing, and electrophysiology are making it possible to study a broader array of organisms, enabling neurobiological comparisons inspired by the vast variation found in nature. We argue that a nature-inspired approach to neuroscience that considers the full diversity of brains in the animal kingdom will lead to new and unexpected biological discoveries, help reveal general principles of nervous system organization, and expand the breadth of biological phenomena that can be uncovered.
Learning enables organisms to adapt to a dynamic world by forming and updating internal representations of their environment. Statistical Learning (SL) and Reinforcement Learning (RL) offer complementary perspectives on this process. RL is fundamentally goal-directed, focused on maximizing rewards through Reward Prediction Error (RPE). SL extracts the statistical structure of the environment without explicit instruction or reinforcement. Model-Based RL additionally incorporates State Prediction Error (SPE) to refine an internal model of the world, overlapping with SL, which may use SPE or associative mechanisms devoid of error computations to extract structure. Neurobiologically, current research shows that RL is linked to midbrain dopaminergic signaling, whereas SL is supported by cortical and subcortical networks including early sensory areas and the hippocampus. This review compares RL and SL across their historical foundations, objectives, computational principles, and neural implementation, suggesting ways to better delineate the boundaries and interconnections between these two fundamental forms of learning.
Parkinson's disease (PD) is a progressive neurological disorder that primarily affects motor movement but also causes a range of non-motor symptoms. Characteristic hallmarks include progressive loss of dopaminergic neurons in the substantia nigra pars compacta and the buildup of intracellular protein aggregates known as Lewy bodies containing the protein alpha-synuclein. While the etiology of PD is multifactorial, aging is widely recognized as the most significant risk factor for the disorder. Cellular senescence, a hallmark of aging, has in recent years emerged as a potential driver of neurodegeneration associated with PD. Senescent cells accumulate in the aging brain, exhibit a proinflammatory secretory phenotype, and are believed to contribute to the pathogenesis of PD through multiple mechanisms. This mini-review examines the evidence linking cellular senescence to PD, discusses potential underlying molecular and cellular mechanisms involved in this process, and evaluates the therapeutic potential of targeting senescent cells for treatment of the disorder.
The sense of touch provides spatial and textural information but how tactile input is integrated with motor signals and selectively processed for adaptive behavior remain incompletely understood. Here, I review neuronal circuits contributing to whisker sensation in rodents at three different levels. First, I point to the columnar architecture of the primary somatosensory cortex in which whiskers are individually represented by barrel columns with intricate microcircuits, likely important for processing tactile information from individual whiskers. Next, I highlight the importance of lateral interactions within the barrel cortex, which likely underlie shape perception as multiple whiskers sample an object surface. Finally, I consider the growing body of research implicating brain-wide interactions to support adaptive behavior, specifically focusing on roles of long-range projection neurons from the barrel cortex to a large number of downstream brain regions. Such brain-wide communication poses important challenges for experimental, computational and theoretical neuroscience necessitating new technical and conceptual advances.
Traditional laboratory assays are limited in capturing the full range of evolved brain function, particularly in the domain of social behavior. While laboratory approaches offer control and causal precision, they constrain how animals interact through artificial groupings and the elimination of sociospatial structure. Here, we outline an emerging complementary approach-field neuroethology-which investigates neural mechanisms of behavior and their socioecological consequences in organisms living within semi-natural or natural contexts. We define its aims, highlight promising domains for its application, and note the technical innovations enabling its practice. Rather than framing field neuroethology in opposition to laboratory studies, we emphasize its potential to broaden the questions we can ask about neurobehavioral relationships-particularly those related to ecological validity and real-world fitness outcomes. Field neuroethology is not a replacement for traditional approaches, but rather an expansion of the experimental toolkit for investigating neurobehavioral functions expressed only in dynamic socioecological contexts.
Visual perception is imperfect. Fortunately, the environment contains an incredible amount of structure that observers can learn to exploit through experience. As a result, representations of the environment that are perceived and stored in memory are often biased towards or away from other stimuli, reference points, and expectations that are acquired across varying timescales. We propose that this myriad of bias phenomena reflects a shared adaptive principle where the brain optimizes its noisy sensory representations to support behavior within its resource constraints. Under this principle, experience is stabilized through the integration of mutually informative stimuli, whilst the discriminability between stimuli is maintained through adaptation. We suggest that this principle emerges naturally from predictive and adaptive coding frameworks that can operate across multiple levels of processing and predict attraction and repulsion even within the same task or stimulus. Viewing these biases as emergent properties of a hierarchical statistical learning system offers new insight into how the brain balances stability and flexibility when shaping our perception and memory of the world.
Across taxa, social animals inevitably encounter dying or dead conspecifics and respond in patterned ways, yet the mechanisms underlying these behaviors remain understudied. Bees offer a powerful comparative system for exploring the neuroethology of corpse-directed behaviors. Across the bee phylogeny, sociality has been gained and lost multiple times, resulting in species that range from solitary to highly eusocial. As nesting became increasingly communal, bees evolved diverse corpse-directed behaviors including avoidance, transport and removal, cannibalism, and burial. These behaviors are thought to mitigate pathogen and predation risks, influence resource allocation, and shape colony functioning. In this review, we synthesize findings on corpse-directed behaviors across bee species and social systems. We examine the emerging neurobiological, sensory, endocrine, molecular, and social mechanisms that support corpse detection and behavioral specialization. Lastly, we highlight key gaps in existing research and priorities for future work on the neurobiological and evolutionary foundations of corpse-directed behaviors.
Context-dependent decision-making enables flexible behavior by allowing identical sensory inputs to guide different actions depending on memory, rules, or goals. Recent advances in large-scale neural recordings have shifted the focus from single-neuron tuning to population-level representations, revealing principles by which neural populations support such flexibility. Here, we review evidence of how context-dependent decisions are implemented by population coding mechanisms, including nonlinear mixed selectivity, task-dependent population geometry, shared representational subspaces, and structured across-neuron correlations. Nonlinear mixed selectivity expands representational dimensionality, allowing downstream readout of arbitrary combinations of task variables. Learning reshapes population geometry, and it may balance flexibility and generalization by promoting the reuse of shared representations when task components overlap. Structured correlations between neurons that share a projection target enhance transmission of context-dependent information to downstream circuits. These population-level coding mechanisms provide a conceptual framework for understanding how neural circuits integrate sensory and contextual information to guide behavior.