Understanding the Brain's Sensory Processing Through Integrated Frameworks
Perception stands as one of the brain's most remarkable achievements, transforming vast arrays of sensory data from the environment into coherent, actionable internal representations. A new review article by Jerry L. Chen, affiliated with the Department of Biology at Boston University, delves deeply into this process. Titled "Learning how to experience the world: From circuits to cell types to genes," the piece appears in the August 2026 issue of Current Opinion in Neurobiology. It examines how distinct neural circuits, molecularly defined cell types, and specific gene expression programs work together to support two complementary models of sensory perception: representational processing and predictive processing.
The full publication is available at https://www.sciencedirect.com/science/article/abs/pii/S0959438826000759. Chen's analysis draws on recent mouse model studies to propose that cell-type-specific transcriptional programs serve as a mechanistic bridge between these frameworks, enabling the brain to handle both bottom-up feature integration and top-down prediction-error signaling in a state-dependent manner.
Representational and Predictive Processing: Complementary Views of Perception
Sensory perception begins with high-dimensional inputs—think of the countless edges, colors, sounds, and textures bombarding the senses at any moment. The brain compresses this complexity into low-dimensional internal models that guide adaptive behavior. Two major theoretical frameworks have shaped understanding of this transformation.
Representational processing describes a hierarchical flow where primary sensory areas detect basic features, such as edge orientation in vision or frequency in audition. Higher-order regions then integrate these into complex objects and concepts. Predictive processing, in contrast, posits that the brain maintains an internal generative model of the world, sending top-down predictions that are compared against incoming sensory signals. Mismatches generate prediction errors that update the model.
Chen argues these are not competing explanations but complementary modes that the same circuits can support depending on behavioral context. During active engagement, circuits may emphasize representational stability or error detection; during rest, they facilitate model consolidation and updating. This integration offers a more unified account of how perception supports learning and memory.
Cell Types as the Building Blocks of Circuit Function
Advances in single-cell transcriptomics have revealed that the neocortex comprises dozens of molecularly distinct cell types, defined by combinatorial patterns of gene expression. These types include excitatory projection neurons and diverse inhibitory interneurons, each with unique wiring patterns, electrophysiological properties, and plasticity rules.
In primary sensory cortices, specific cell types act as stable feature detectors, while others respond preferentially to prediction errors. For example, certain excitatory neurons maintain consistent responses to sensory stimuli even as experience changes, preserving representational fidelity. Distinct inhibitory populations modulate circuit dynamics to support either associative learning or error-based updates.
Higher-order association areas show even greater diversity in inhibitory cell-type composition, with gene expression profiles that shift during learning. These profiles influence how circuits balance stability for maintaining representations against flexibility for incorporating new information. Chen highlights how such specialization allows circuits to toggle between processing modes without requiring entirely separate hardware.
Transcriptional Programs Linking Genes to Computation
Gene expression is not static. While some genes define stable cell identity, others respond dynamically to activity, experience, or neuromodulatory signals. Chen reviews evidence that these activity-regulated transcriptional programs equip cell types with the computational properties needed for both representational and predictive functions.
Studies combining in vivo calcium imaging with post-hoc spatial transcriptomics have begun mapping which genes correlate with specific functional properties. In one line of work, neurons showing persistent responses during altered sensory experience express particular immediate early genes and homeostatic regulators. In association cortex, plasticity-related genes differ between cell types supporting associative versus error learning.
The cholinergic system provides a compelling example. Acetylcholine release varies with behavioral state, influencing gene expression in targeted cell types and thereby shifting circuit operations from one processing mode to another. This state-dependence may explain how the same cortical networks support active sensation during wakefulness and memory consolidation during quiescence.
Photo by National Cancer Institute on Unsplash
Insights from Mouse Primary Sensory and Association Cortex
Chen synthesizes findings primarily from mouse visual and somatosensory cortices, as well as higher-order areas like the retrosplenial and prefrontal cortices. In primary areas, molecularly defined subclasses exhibit distinct tuning properties: some function as reliable encoders of stimulus features, while others signal deviations from expected input.
Work on Baz1a-expressing excitatory neurons, for instance, demonstrates a circuit hub that recruits local networks during salient sensory events while preserving overall representational stability. Inhibitory cell types, classified by transcriptomic profiles, differentially control the flow of bottom-up versus top-down signals.
In association cortex, the balance of specific interneuron subtypes correlates with the capacity for both forms of learning. Gene expression changes during training further refine these capabilities. These observations suggest that examining the intersection of circuit anatomy, cell-type identity, and dynamic gene programs offers a powerful lens for understanding perception.
Methodological Advances Enabling Multi-Level Analysis
Progress in this area stems from converging technologies. Transgenic driver lines and enhancer AAV vectors allow precise genetic access to individual cell types. When paired with two-photon imaging in behaving animals, researchers can record activity from genetically identified populations.
Post-hoc spatial transcriptomics then links functional data to the full gene expression profile of each recorded neuron. New biochemical sensors for intracellular signaling and neuromodulators add another layer, revealing how transcriptional programs are engaged in real time.
Chen notes that future causal tests will require combining these readouts with targeted perturbations of specific genes within defined cell types. Such experiments can establish whether particular transcriptional modules are necessary for representational stability, prediction-error signaling, or the switch between them.
Broader Implications for Neuroscience Research and Education
This integrative approach has significant ramifications for the field. It moves beyond treating cell-type markers as mere labels and instead asks how the genes themselves shape computation. It also provides a framework for reconciling long-standing debates between bottom-up and top-down accounts of cortical function.
For researchers and trainees, the work underscores the value of interdisciplinary training that spans molecular biology, systems neuroscience, and computational modeling. Laboratories equipped for simultaneous functional imaging and transcriptomics are increasingly central to advancing understanding of brain function.
Funding bodies such as the NIH BRAIN Initiative have supported much of the foundational work cited, highlighting sustained investment in technologies that bridge scales from genes to behavior. Academic programs preparing the next generation of neuroscientists benefit from curricula that emphasize these multi-level methods.
Future Directions and Open Questions
Chen outlines several promising avenues. Expanding the approach to additional sensory modalities and species will test the generality of the proposed mechanisms. Longitudinal studies tracking gene expression changes across learning and aging could reveal how transcriptional programs support lifelong adaptation.
Integration with emerging tools for measuring extra- and intracellular signaling in vivo promises tighter links between molecular events and circuit dynamics. Computational models that incorporate cell-type-specific gene regulatory networks may generate testable predictions about when and how circuits switch between representational and predictive modes.
Ultimately, these efforts aim to explain not only how the brain experiences the world but also how disruptions in these processes contribute to neurological and psychiatric conditions involving perceptual or learning deficits.
Relevance to Academic Careers in Neuroscience
For PhD students and early-career researchers, Chen's review illustrates the trajectory of modern neuroscience: from descriptive catalogs of cell types to mechanistic understanding of how molecular programs implement complex computations. Positions in systems neuroscience increasingly value expertise in both advanced imaging and molecular techniques.
University departments and research institutes seeking faculty in this area often prioritize candidates who can bridge scales and leverage large-scale transcriptomic datasets. Postdoctoral fellowships supported by initiatives like the BRAIN Initiative provide structured pathways for gaining these integrated skills.
Resources on academic job boards highlight growing demand for researchers who can translate basic findings on circuit function into insights relevant to behavior and disease. Engaging with primary literature such as Chen's work helps aspiring academics identify high-impact questions and methodological toolkits.
