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Search · four archives
12 papers · ranked by Valyu relevance
Josiah Kratz, Zihang Wen, Shiladitya Banerjee, Oana Carja
Bacterial populations display extraordinary resilience to antibiotic stress, driven by diverse physiological states that allow some cells to persist and later repopulate. This phenotypic heterogeneity, amplified by environmental fluctuations, undermines the effectiveness of conventional fixed-dose treatment regimens.…
F. Hafna Ahmed, Asher Bender, Asiri Wijesinghe, Allen Zhu + 11 more
Enzymes are essential biocatalysts across diverse industries, driving demand for high-performing variants. Foundation models are attractive for guiding enzyme discovery, but often lack the resolution to model subtle variations driving function within homologous families. Navigating these rugged functional landscapes to…
Daniel Dell’uomo, Andrew Satz, Brett Averso
Antibody discovery remains constrained by resource-intensive experimental screening approaches that offer limited control over critical properties. Here we present HyperBind2, a machine learning platform that progressively improves antibody-antigen interaction predictions through experimental feedback cycles. Unlike…
Muhammad Noman Almani, Shreya Saxena
Neural populations display complex response patterns with marked transitions between distinct underlying computational strategies on very short timescales during motor tasks. Such complex-yet-structured dynamical strategies may reflect computational needs of neural systems, shaped by optimal feedback and autonomous…
W. Alex Foxworthy
Across minimal neural networks and small transformer models, we demonstrate that experience ordering alone can produce durable, irreversible behavioral divergence in artificial agents—but only when learning is consolidated into internal parameters rather than externally scaffolded. We test six architectural variants…
Jianning Chen, Masakazu Taira, Kenji Doya
Behavioral strategies can change in response to environmental and internal states, either gradually or abruptly, enabling flexible adaptation. Such strategy regulation is central to meta-learning, the ability to learn to learn. Previous studies analyzed temporal or condition-dependent strategy change using models and…
Jun Kobayashi
We ask how a forward-model-based predictive state observer should set its sensory prediction-error correction gain during muscle-driven reaching, and whether that gain can be adapted from agent-available signals — innovation history and per-episode reaching outcome — rather than from swept oracle labels. We evaluate a…
Oz Shaul, Tali Ilovitsh
Beam shaping of ultra-short pulses is essential for medical ultrasound, where single-cycle excitations are required to achieve high axial resolution and improve frame rate. Conventional methods, such as the Gerchberg–Saxton (GS) algorithm or more recent deep learning approaches, are generally effective for…
Yuxuan Luo, Kunlin Wei
Motor adaptation arises from multiple learning mechanisms, including use-dependent learning (UDL) driven by repetition and implicit error-based learning (EBL) driven by motor prediction errors. Although both mechanisms contribute implicitly to shaping movement execution, whether these two mechanisms interact remains…
Francesca Greenstreet, Jesse P. Geerts, Juan A. Gallego, Claudia Clopath
The initial stage of learning motor skills involves exploring vast action spaces, making it impractical to learn the value of every possible action independently. This poses a challenge for standard reinforcement learning approaches, which excel in constrained domains but struggle when the space of possible actions is…
Russell Jeter, Dmitrii Todorov, Yaroslav Molkov
A clinician guiding a stroke patient through a 45-minute rehabilitation session, a coach planning a training day, a teacher choosing the order of practice problems, they all face the same question: “given everything practiced so far, what should the next trial be?” The motor-learning literature offers two coarse…
Menno van Laarhoven, Alfredo Rates, Josiah B. Passmore, Shengling Shi + 3 more
Optogenetics enables experiments in out-of-equilibrium conditions to clarify biological mechanisms and quantify biophysical parameters. However, modelling and control techniques to study mammalian cell biology under optogenetic perturbation remain underutilised. Here, we benchmark these methods within mammalian cells…