Memory Alone Does Not Account for the Way Rats Learn a Simple Spatial Alternation Task

Female rodents in behavioral neuroscience: Narrative review on the methodological pitfalls

Since the NIH 'Sex as biological variable' policy, the percentage of studies including female subjects have increased largely. Nonetheless, many researchers fail to adequate their protocols to include females. In this narrative review, we aim to discuss the methodological pitfalls of the inclusion of female rodents in behavioral neuroscience. We address three points to consider in studies: the manipulations conducted only in female animals (such as estrous cycle monitoring, ovariectomy, and hormone replacement), the consideration of males as the standard, and biases related to interpretation and publication of the results. In addition, we suggest guidelines and perspectives for the inclusion of females in preclinical research.

Persistent Impact of Prior Experience on Spatial Learning

Learning to solve a new problem involves identifying the operating rules, which can be accelerated if known rules generalize in the new context. We ask how prior experience affects learning a new rule that is distinct from known rules. We examined how rats learned a new spatial navigation task after having previously learned tasks with different navigation rules. The new task differed from the previous tasks in spatial layout and navigation rule. We found that experience history did not impact overall performance. However, by examining navigation choice sequences in the new task, we found experience-dependent differences in exploration patterns during early stages of learning, as well as differences in the types of errors made during stable performance. The differences were consistent with the animals adopting experience-dependent memory strategies to discover and implement the new rule. Our results indicate prior experience shapes the strategies for solving novel problems, and the impact of prior experience remains persistent.

The choice-wide behavioral association study: data-driven identification of interpretable behavioral components

Behavior contains rich structure across many timescales, but there is a dearth of methods to identify relevant components, especially over the longer periods required for learning and decision-making. Inspired by the goals and techniques of genome-wide association studies, we present a data-driven method-the choice-wide behavioral association study: CBAS-that systematically identifies such behavioral features. CBAS uses a powerful, resampling-based, method of multiple comparisons correction to identify sequences of actions or choices that either differ significantly between groups or significantly correlate with a covariate of interest. We apply CBAS to different tasks and species (flies, rats, and humans) and find, in all instances, that it provides interpretable information about each behavioral task.

The Neurodata Without Borders ecosystem for neurophysiological data science

The neurophysiology of cells and tissues are monitored electrophysiologically and optically in diverse experiments and species, ranging from flies to humans. Understanding the brain requires integration of data across this diversity, and thus these data must be findable, accessible, interoperable, and reusable (FAIR). This requires a standard language for data and metadata that can coevolve with neuroscience. We describe design and implementation principles for a language for neurophysiology data. Our open-source software (Neurodata Without Borders, NWB) defines and modularizes the interdependent, yet separable, components of a data language. We demonstrate NWB's impact through unified description of neurophysiology data across diverse modalities and species. NWB exists in an ecosystem, which includes data management, analysis, visualization, and archive tools. Thus, the NWB data language enables reproduction, interchange, and reuse of diverse neurophysiology data. More broadly, the design principles of NWB are generally applicable to enhance discovery across biology through data FAIRness.

Spatial preferences account for inter-animal variability during the continual learning of a dynamic cognitive task

Understanding the complexities of behavior is necessary to interpret neurophysiological data and establish animal models of neuropsychiatric disease. This understanding requires knowledge of the underlying information-processing structure—something often hidden from direct observation. Commonly, one assumes that behavior is solely governed by the experimenter-controlled rules that determine tasks. For example, differences in tasks that require memory of past actions are often interpreted as exclusively resulting from differences in memory. However, such assumptions are seldom tested. Here, we provide a comprehensive examination of multiple processes that contribute to behavior in a prevalent experimental paradigm. Using a combination of behavioral automation, hypothesis-driven trial design, and reinforcement learning modeling, we show that rats learn a spatial alternation task consistent with their drawing upon spatial preferences in addition to memory. Our approach also distinguishes learning based on established preferences from generalization of task structure, providing further insights into learning dynamics.

Spatial alternation behaviors are commonly used to measure memory. Kastner et al. use experimental and computational approaches to show that rats learn spatial alternation in a manner consistent with their utilizing multiple computational features in addition to just memory and that variation in use of these features underlies inter-animal variability.