Emmanouil Froudarakis

Sensory neurons are traditionally viewed as feature detectors that respond with an increase in firing rate to preferred stimuli while remaining unresponsive to others. Here, we identify a dual-feature encoding strategy in macaque visual cortex, wherein many neurons in areas V1 and V4 are selectively tuned to two distinct visual features—one that enhances and one that suppresses activity—around an elevated baseline firing rate. By combining neuronal recordings with functional digital twin models—deep learning-based predictive models of biological neurons—we were able to systematically identify each neuron’s preferred and non-preferred features. These feature pairs served as anchors for a continuous, low-dimensional axis in natural image similarity space, along which neuronal activity varied approximately linearly. Within a single visual area, visual features that strongly or weakly activated individual neurons also had a high probability of modulating the activity of other neurons, suggesting a shared feature selectivity across the population that structures stimulus encoding. We show that this encoding strategy is conserved across species, present in both primary and lateral visual areas of mouse cortex. Dual-feature selectivity is consistent with recent anatomical evidence for feature-specific inhibitory connectivity, complementing the feature-detector principle through circuit mechanisms in which selective excitation and inhibition may together enhance the representational capacity of the neuronal population.

Sensory neurons are traditionally viewed as feature detectors that respond with an increase in firing rate to preferred stimuli while remaining unresponsive to others. Here, we identify a dual-feature encoding strategy in macaque visual cortex, wherein many neurons in areas V1 and V4 are selectively tuned to two distinct visual features—one that enhances and one that suppresses activity—around an elevated baseline firing rate. By combining neuronal recordings with functional digital twin models—deep learning-based predictive models of biological neurons—we were able to systematically identify each neuron’s preferred and non-preferred features. These feature pairs served as anchors for a continuous, low-dimensional axis in natural image similarity space, along which neuronal activity varied approximately linearly. Within a single visual area, visual features that strongly or weakly activated individual neurons also had a high probability of modulating the activity of other neurons, suggesting a shared feature selectivity across the population that structures stimulus encoding. We show that this encoding strategy is conserved across species, present in both primary and lateral visual areas of mouse cortex. Dual-feature selectivity is consistent with recent anatomical evidence for feature-specific inhibitory connectivity, complementing the feature-detector principle through circuit mechanisms in which selective excitation and inhibition may together enhance the representational capacity of the neuronal population.

Despite variations in appearance, we recognize objects robustly. Neuronal populations responding to objects presented under varying transformations form object manifolds, and hierarchically organized visual areas are hypothesized to untangle pixel intensities into linearly decodable object representations. However, the associated changes in the geometry of the object manifolds along the cortex remain unknown. Using home cage training, we showed that mice are capable of invariant object recognition. We recorded the responses of thousands of neurons to measure the information about object identity across the visual cortex and found that the lateral areas LM, LI, and AL carry more linearly decodable object information compared to other visual areas. We applied the theory of linear separability of manifolds and found that the increase in classification capacity is associated with a decrease in the dimension and radius of the object manifold, identifying the key features in the geometry of the population neural code that are associated with invariant object coding.

Sensory systems support generalization by representing features that persist under input variation; however, identifying the neuronal basis of these invariances remains difficult due to high-dimensional and nonlinear neural computations. Here we leverage the inception loop paradigm, iterating between large-scale recordings, predictive models and in silico experiments with in vivo verification, to characterize neuronal invariances in mouse primary visual cortex (V1). We synthesize varied exciting inputs (VEIs), dissimilar images that drive target neurons. These VEIs revealed a new bipartite invariance: one subfield encodes a shift-tolerant high-frequency texture and the other encodes a fixed low-frequency pattern. This division aligns with object boundaries defined by spatial frequency differences in highly activating images, suggesting a contribution to segmentation. Analysis of the MICrONS dataset revealed a hierarchy of excitatory neurons in mouse V1 layers 2/3: postsynaptic neurons exhibited greater invariance than their presynaptic inputs, while neurons with lower invariance formed more connections. Together, these results provide insights and scalable methodology for mapping neuronal invariances.

Neuroscience thrives on diversity—not only in the questions it asks but also in the models it uses to explore them. Across the field, different animal models have played pivotal roles in uncovering the principles governing brain function, development, and disease. Yet, the choice of model organisms remains a subject of debate. This editorial highlights the importance of embracing a wide range of animal models in neuroscience research. Each model offers unique strengths aligned with particular experimental approaches and scientific questions, contributing complementary insights that no single species alone can provide. By leveraging this diversity, we can achieve a more comprehensive understanding of the brain across levels of organization, from molecular pathways to behavioral outputs at the organismal level. Beyond the scientific advantages, we also discuss ethical and practical considerations: A diverse approach can promote responsible animal use by tailoring species choice to specific research goals. It can also foster environmental sustainability by avoiding unnecessary duplication of effort and resources. We call on neuroscientists to reflect on the value of integrating insights across species and experimental approaches. By moving beyond entrenched preferences and disciplinary silos, the field can unlock new opportunities for discovery. In championing the use of diverse animal models, we aim to inspire a more inclusive, efficient, and impactful neuroscience that rises to the complexity of its subject.

As brain activity is tightly coupled to behavior, an accurate understanding of neural function necessitates consideration of behavioral tasks that capture the complexity and variety animals encounter. Nevertheless, animal training for behavioral experiments is often labor-intensive, costly, and difficult to standardize. To overcome these challenges, we developed EthoPy, an open-source, Python-based behavioral control framework that integrates stimulus presentation, hardware management, and data logging. EthoPy supports diverse behavioral paradigms, stimulus modalities, and experimental systems, from homecage to head-fixed configurations, while operating on affordable hardware, such as Raspberry Pi. Its modular architecture and database integration enable scalable, high-throughput automatic behavioral training with minimal experimenter involvement while ensuring reproducibility through comprehensive metadata tracking. By automating training workflows, EthoPy makes it feasible to implement sophisticated behavioral paradigms that are traditionally difficult to achieve. EthoPy thus provides an accessible, extensible framework to study behavior and the underlying neural activity.

Sensory neurons are traditionally viewed as feature detectors that respond with an increase in firing rate to preferred stimuli while remaining unresponsive to others. Here, we identify a dual-feature encoding strategy in macaque visual cortex, wherein many neurons in areas V1 and V4 are selectively tuned to two distinct visual features—one that enhances and one that suppresses activity—around an elevated baseline firing rate. By combining neuronal recordings with functional digital twin models—deep learning-based predictive models of biological neurons—we were able to systematically identify each neuron’s preferred and non-preferred features. These feature pairs served as anchors for a continuous, low-dimensional axis in natural image similarity space, along which neuronal activity varied approximately linearly. Within a single visual area, visual features that strongly or weakly activated individual neurons also had a high probability of modulating the activity of other neurons, suggesting a shared feature selectivity across the population that structures stimulus encoding. We show that this encoding strategy is conserved across species, present in both primary and lateral visual areas of mouse cortex. Dual-feature selectivity is consistent with recent anatomical evidence for feature-specific inhibitory connectivity, complementing the feature-detector principle through circuit mechanisms in which selective excitation and inhibition may together enhance the representational capacity of the neuronal population.

Nonhuman animal models continue to be indispensable in neuroscience research for the foreseeable future. In recent years, animal‐right activists have been increasing the pressure on politicians and policymakers to phase out animal research. To address this pressure, we should adapt our communication habits, to be more open and transparent about (our) animal research and most importantly to expand the methods we use to communicate about our research and increase the extend of this outreach. In this editorial, we discuss the why and how of animal research communication.

We are now in the era of millimeter-scale electron microscopy (EM) volumes collected at nanometer resolution (Shapson-Coe et al., 2021; Consortium et al., 2021). Dense reconstruction of cellular compartments in these EM volumes has been enabled by recent advances in Machine Learning (ML) (Lee et al., 2017; Wu et al., 2021; Lu et al., 2021; Macrina et al., 2021). Automated segmentation methods produce exceptionally accurate reconstructions of cells, but post-hoc proofreading is still required to generate large connectomes free of merge and split errors. The elaborate 3-D meshes of neurons in these volumes contain detailed morphological information at multiple scales, from the diameter, shape, and branching patterns of axons and dendrites, down to the fine-scale structure of dendritic spines. However, extracting these features can require substantial effort to piece together existing tools into custom workflows. Building on existing open-source software for mesh manipulation, here we present “NEURD”, a software package that decomposes meshed neurons into compact and extensively-annotated graph representations. With these feature-rich graphs, we automate a variety of tasks such as state of the art automated proofreading of merge errors, cell classification, spine detection, axon-dendritic proximities, and other annotations. These features enable many downstream analyses of neural morphology and connectivity, making these massive and complex datasets more accessible to neuroscience researchers focused on a variety of scientific questions.