Sophia Sanborn

All Publications

• Functional bipartite invariance in mouse primary visual cortex receptive fields. Nature neuroscience Ding, Z., Tran, D., Ponder, K., Ding, Z., Froebe, R., Ntanavara, L., Fahey, P. G., Cobos, E., Baroni, L., Diamantaki, M., Wang, E. Y., Chang, A., Papadopoulos, S., Fu, J., Muhammad, T., Papadopoulos, C., Cadena, S. A., Evangelou, A., Willeke, K., Anselmi, F., Sanborn, S., Antolik, J., Froudarakis, E., Patel, S., Walker, E. Y., Reimer, J., Sinz, F. H., Ecker, A. S., Franke, K., Pitkow, X., Tolias, A. S. 2026

  Abstract

  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.

  View details for DOI 10.1038/s41593-026-02213-3

  View details for PubMedID 41741659

  View details for PubMedCentralID 2678572

• A wireless subdural-contained brain-computer interface with 65,536 electrodes and 1,024 channels NATURE ELECTRONICS Jung, T., Zeng, N., Fabbri, J. D., Eichler, G., Li, Z., Zabeh, E., Das, A., Willeke, K., Wingel, K. E., Dubey, A., Huq, R., Sharma, M., Hu, Y., Ramakrishnan, G., Tien, K., Mantovani, P., Parihar, A., Yin, H., Oswalt, D., Misdorp, A., Uguz, I., Shinn, T., Rodriguez, G. J., Nealley, C., Van Der Molen, T., Sanborn, S., Gonzales, I., Roukes, M., Knecht, J., Kosik, K. S., Yoshor, D., Canoll, P., Spinazzi, E., Carloni, L. P., Pesaran, B., Patel, S., Jacobs, J., Youngerman, B., Cotton, R., Tolias, A., Shepard, K. L. 2025

  View details for DOI 10.1038/s41928-025-01509-9

  View details for Web of Science ID 001632316000001

• Beyond Euclid: an illustrated guide to modern machine learning with geometric, topological, and algebraic structures. Machine learning: science and technology Papillon, M., Sanborn, S., Mathe, J., Cornelis, L., Bertics, A., Buracas, D., J Lillemark, H., Shewmake, C., Dinc, F., Pennec, X., Miolane, N. 2025; 6 (3): 031002

  Abstract

  The enduring legacy of Euclidean geometry underpins classical machine learning, which, for decades, has been primarily developed for data lying in Euclidean space. Yet, modern machine learning increasingly encounters richly structured data that is inherently non-Euclidean. This data can exhibit intricate geometric, topological and algebraic structure: from the geometry of the curvature of space-time, to topologically complex interactions between neurons in the brain, to the algebraic transformations describing symmetries of physical systems. Extracting knowledge from such non-Euclidean data necessitates a broader mathematical perspective. Echoing the 19th-century revolutions that gave rise to non-Euclidean geometry, an emerging line of research is redefining modern machine learning with non-Euclidean structures. Its goal: generalizing classical methods to unconventional data types with geometry, topology, and algebra. In this review, we provide an accessible gateway to this fast-growing field and propose a graphical taxonomy that integrates recent advances into an intuitive unified framework. We subsequently extract insights into current challenges and highlight exciting opportunities for future development in this field.

  View details for DOI 10.1088/2632-2153/adf375

  View details for PubMedID 40755551

  View details for PubMedCentralID PMC12315666

• Dual-feature selectivity enables bidirectional coding in visual cortical neurons. bioRxiv : the preprint server for biology Franke, K., Karantzas, N., Willeke, K., Diamantaki, M., Ramakrishnan, K., Elumalai, P., Restivo, K., Fahey, P., Nealley, C., Shinn, T., Garcia, G., Patel, S., Ecker, A., Walker, E. Y., Froudarakis, E., Sanborn, S., Sinz, F. H., Tolias, A. 2025

  Abstract

  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, suggesting a coding strategy in which selective excitation and inhibition increase the representational capacity of the neuronal population.

  View details for DOI 10.1101/2025.07.16.665209

  View details for PubMedID 40777393

• Stable, chronic in-vivo recordings from a fully wireless subdural-contained 65,536-electrode brain-computer interface device. bioRxiv : the preprint server for biology Jung, T., Zeng, N., Fabbri, J. D., Eichler, G., Li, Z., Zabeh, E., Das, A., Willeke, K., Wingel, K. E., Dubey, A., Huq, R., Sharma, M., Hu, Y., Ramakrishnan, G., Tien, K., Mantovani, P., Parihar, A., Yin, H., Oswalt, D., Misdorp, A., Uguz, I., Shinn, T., Rodriguez, G. J., Nealley, C., Sanborn, S., Gonzales, I., Roukes, M., Knecht, J., Yoshor, D., Canoll, P., Spinazzi, E., Carloni, L. P., Pesaran, B., Patel, S., Jacobs, J., Youngerman, B., Cotton, R. J., Tolias, A., Shepard, K. L. 2025

  Abstract

  Minimally invasive, high-bandwidth brain-computer-interface (BCI) devices can revolutionize human applications. With orders-of-magnitude improvements in volumetric efficiency over other BCI technologies, we developed a 50-μm-thick, mechanically flexible micro-electrocorticography (μECoG) BCI, integrating a 256×256 array of electrodes, signal processing, data telemetry, and wireless powering on a single complementary metal-oxide-semiconductor (CMOS) substrate containing 65,536 recording channels, from which we can simultaneously record a selectable subset of up to 1024 channels at a given time. Fully implanted below the dura, our chip is wirelessly powered, communicating bi-directionally with an external relay station outside the body. We demonstrated chronic, reliable recordings for up to two weeks in pigs and up to two months in behaving non-human primates from somatosensory, motor, and visual cortices, decoding brain signals at high spatiotemporal resolution.

  View details for DOI 10.1101/2024.05.17.594333

  View details for PubMedID 38798494

  View details for PubMedCentralID PMC11118429

• Exploring the hierarchical structure of human plans via program generation. Cognition Correa, C. G., Sanborn, S., Ho, M. K., Callaway, F., Daw, N. D., Griffiths, T. L. 2024; 255: 105990

  Abstract

  Human behavior is often assumed to be hierarchically structured, made up of abstract actions that can be decomposed into concrete actions. However, behavior is typically measured as a sequence of actions, which makes it difficult to infer its hierarchical structure. In this paper, we explore how people form hierarchically structured plans, using an experimental paradigm with observable hierarchical representations: participants create programs that produce sequences of actions in a language with explicit hierarchical structure. This task lets us test two well-established principles of human behavior: utility maximization (i.e. using fewer actions) and minimum description length (MDL; i.e. having a shorter program). We find that humans are sensitive to both metrics, but that both accounts fail to predict a qualitative feature of human-created programs, namely that people prefer programs with reuse over and above the predictions of MDL. We formalize this preference for reuse by extending the MDL account into a generative model over programs, modeling hierarchy choice as the induction of a grammar over actions. Our account can explain the preference for reuse and provides better predictions of human behavior, going beyond simple accounts of compressibility to highlight a principle that guides hierarchical planning.

  View details for DOI 10.1016/j.cognition.2024.105990

  View details for PubMedID 39616822