Topic: behavior

This Stanford symposium examines how molecular, cellular, and circuit-level advances in neurodevelopment shape understanding of behavior and health, with scientific talks, awards, and a poster session.

Nedah Nemati examines how neuroscience defines behavior and why freely moving animals and naturalistic experiments matter. She discusses how tools, historical assumptions and researchers' own experience influence scientific questions, including what studies of sleep reveal about the relationship between observation and explanation.

Correlated activity between neurons can cause variability in behavior across trials, as trial-by-trial neuronal noise will propagate through the motor system. The extent to which correlated activity affects behavior depends on the properties of its translation into movement. A major hurdle in studying the effect of neural correlations on behavior is the need to assume a model of the relationship between neural activity and behavior. We developed a novel method that estimates the contribution of correlations to behavior, with minimal assumptions. We applied this method to the frontal eye field

In the brain, the collective activity of neurons form trajectories on low dimensional neural manifolds. The neural computation underlying flexible cognition and behavior relies on dynamic control of these structures. For example, different tasks or behaviors are represented on different subspaces, requiring fast timescale subspace rotation to move from one behavior to the next. For flexibility in a particular behavior, the neural trajectory must be dynamically controllable within that behaviorally determined subspace. Here we propose mechanisms for fast timescale control of the neural subspace

Understanding the circuit mechanisms of brain computation and behavior is one of the central questions in neuroscience. Recent advances in task-optimized recurrent neural networks (RNNs) have opened a new avenue to directly link neural dynamics to behavior and uncover the underlying mechanisms by reverse engineering the trained RNNs. Previous studies leveraged fixed points to compare RNNs across limited tasks and found that while the geometry of neural representations varies, fixed point topology is consistent across RNNs despite differences in model architecture [1]. However, linearizing dyna

The goal of behavioral neuroscience is to understand the relationship between neural activity and behavior. One traditional approach for studying this relationship has been to build models that either translate from the neural activity to behavior (decoding) or from behavior to neural activity (encoding). Recent advances in self-supervised learning now allow for modeling both modalities jointly. In this work, we introduce a multimodal masked modeling approach that masks portions of both behavior and neural activity, using the unmasked data to make predictions across both modalities. We evaluat

How signals relevant to behavior and cognition are processed within neural circuits, engaging thousands of neurons across multiple brain regions remains a central question. After decades of research, our current understanding of brain-wide neural circuits enabling various brain functions in non-rodent species (e.g., cats and primates) remains limited. In stark contrast, significant progress has been made in dissecting mouse brain function, enabled by the availability of transgenic driver and reporter lines.We set out to emulate the quality and reproducibility of transgenic mouse reporter lines