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Biology subjects

Krausz, T. A.

Publications and source records attributed to Krausz, T. A..

4 recordsLinked to original sources

Schrodinger's mouse: human observation erases a drug-induced behavioral phenotype

Animal studies primarily rely on human monitoring for health and behavioral outcomes. The presence of a human, however, can induce stress and potentially alter experimental results. We sought to answer whether human observation alone is enough to change experimental outcomes and obscure the effect of interest. Our automated home-cage behavioral phenotyping system provided the ideal platform to test this hypothesis. Two cohorts received semaglutide (0.03 mg/kg, intraperitoneally) on separate consecutive days. The first cohort was recorded without inspection after dosing; the second underwent a brief visual health check with no handling. Recordings from the checked cohort on the preceding day, without injection or inspection, provided a matched baseline. A single brief check produced large, time-locked changes across the behavioral repertoire. The changes began before the cage door was opened, indicating a response to experimenter proximity. In addition to adding variance, the semaglutide-treated animals' behavior during observation was more similar to the no-injection baseline than to the semaglutide/no-check reference, transiently erasing the drug phenotype. Brief human observation is therefore not a neutral measurement operation; over the minutes surrounding it, it is a perturbation in itself that is large enough to mask the drug effect it is meant to record.

animal behavior and cognition↗

Meta-learning is expressed through altered prefrontal cortical dynamics

Learning where and when rewards like food and water are available is essential for survival1,2. In the simplest cases where resource availability is stable, animals can learn reward contingencies by integrating outcomes across repeated samples of each possible action. In more natural settings, however, reward availability is governed by structured higher-order rules such as depletion and repletion over time. To adapt flexibly to such changing environments, optimal choices require meta-learning wherein animals learn how to learn from external feedback, ultimately enabling them to infer the underlying reward structure from abstract, generalizable rules rather than relying solely on recent outcomes3,4. The existence of meta-learning in animal behavior is well established3-8, yet the neural circuits and computations that implement it remain poorly understood9-11. Here we investigated meta-learning using a spatial foraging task in which rats acquired a depletion-repletion rule that regulated reward availability, and carried out longitudinal, high-density recordings from the medial prefrontal cortex (mPFC). We show that meta-learning engages specific, systematic changes in mPFC neural dynamics that embed the learned rule and thereby alter how the network learns action values from reward outcomes. These dynamics are based on mixed coding of task structure and value in individual mPFC neurons. At the population level, this coding organizes into low-dimensional dynamical motifs that generalize across task conditions. As meta-learning progresses, these motifs are reshaped to instantiate both rule-guided inference of future states before outcome delivery and rule-based value updating during the outcome period. These results indicate that meta-learning sculpts pre-existing prefrontal dynamics to support the acquisition of new, generalizable reward-learning strategies.

neuroscience↗

Hippocampal representations of alternative possibilities are flexibly generated to meet cognitive demands

The cognitive ability to go beyond the present to consider alternative possibilities, including potential futures and counterfactual pasts, can support adaptive decision making. Complex and changing real-world environments, however, have many possible alternatives. Whether and how the brain can select among them to represent alternatives that meet current cognitive needs remains unknown. We therefore examined neural representations of alternative spatial locations in the rat hippocampus during navigation in a complex patch foraging environment with changing reward probabilities. We found representations of multiple alternatives along paths ahead and behind the animal, including in distant alternative patches. Critically, these representations were modulated in distinct patterns across successive trials: alternative paths were represented proportionate to their evolving relative value and predicted subsequent decisions, whereas distant alternatives were prevalent during value updating. These results demonstrate that the brain modulates the generation of alternative possibilities in patterns that meet changing cognitive needs for adaptive behavior.

neuroscience↗

Dual credit assignment processes underliedopamine signals in a complex spatialenvironment.

Dopamine in the nucleus accumbens helps motivate behavior based on expectations of future reward ("values"). These values need to be updated by experience: after receiving reward, the choices that led to reward should be assigned greater value. There are multiple theoretical proposals for how this credit assignment could be achieved, but the specific algorithms that generate updated dopamine signals remain uncertain. We monitored accumbens dopamine as freely behaving rats foraged for rewards in a complex, changing environment. We observed brief pulses of dopamine both when rats received reward (scaling with prediction error), and when they encountered novel path opportunities. Furthermore, dopamine ramped up as rats ran towards reward ports, in proportion to the value at each location. By examining the evolution of these dopamine place-value signals, we found evidence for two distinct update processes: progressive propagation along taken paths, as in temporal-difference learning, and inference of value throughout the maze, using internal models. Our results demonstrate that within rich, naturalistic environments dopamine conveys place values that are updated via multiple, complementary learning algorithms.

neuroscience↗