Topic: behavioral data

This NIH BRAIN Initiative funding opportunity supports new or substantially advanced theories, mechanistic or predictive models, and computational or statistical methods that improve quantitative understanding of brain function across scales. Tools must address complex neural and behavioral data and be made broadly available to the neuroscience research community.

Characterizing the relationship between neural population activity and behavioral data is a central goal of neuroscience. While latent variable models (LVMs) are successful in describing high dimensional data, they are typically only designed for a single type of data, making it difficult to identify structure shared across different experimental data modalities. Here, we address this shortcoming by proposing an LVM which extracts shared and independent latents for distinct, simultaneously recorded experimental modalities. We do this by combining Gaussian Process Factor Analysis (GPFA), an int

Mapping behavioral actions to neural activity is a fundamental goal of neuroscience. As our ability to record large neural and behavioral data increases, there is growing interest in modeling neural dynamics during adaptive behaviors to probe neural representations (Urai et al., 2022; Krakauer et al., 2017). In particular, neural latent embeddings can reveal underlying correlates of behavior, yet, we lack non-linear techniques that can explicitly and flexibly leverage joint behavior and neural data. Here, we fill this gap with a novel contrastive learning method that jointly uses time-series d

Compared to other brain functions (like remembering and deciding), how the brain reflects mood is much less well understood. One reason is that there is no objective right answer to the question, "How happy are you?" Consequently, mood cannot be studied by benchmarking against percent correct on behavioral tasks. While challenging, we cannot shy away from understanding mood given that ~20\% of adults will experience a mood disorder at some point, and for ~30\%, existing treatments will be ineffective. Here, we build on work that modulates, probes, and models fluctuations in subjective happines

Analysis of behavioral data usually averages responses across subjects and/or across trials. However, this approach obscures the actual dynamics of learning, which can occur within few trials and differ between subjects. For instance, reexamination of previous data (Uengoer and Lachnit, 2006) reveals that during learning subjects do not change their responses gradually, but abruptly (Fig. A). These “jumps” occur at different trials for each subject and averaging therefore paints a misleading picture of gradual learning curves. These data are not easily accounted for with simple associative lea

Electrophysiological experiments include metadata, behavioral data, and neural recordings, requiring precise methods to synchronize the different sources. In addition, analysis often involves complex mathematical functions that cannot be applied by non-experts. The massive amount of data generated by long recordings and hundreds of electrode sites adds to the complexity and highlights the need for a workflow that enables standardized and large-scale data analysis.To address these challenges, we developed a Python-based workflow with a graphical user interface using PyQt5. It uses a modular fra