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Kevin Luxem

Publications and source records attributed to Kevin Luxem.

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Ecologically mapped neuronal identity: Towards standardizing activity across heterogeneous experiments

The brain's diversity of neurons enables a rich behavioral repertoire and flexible adaptation to new situations. Assuming that the ecological pressure has optimized this neuronal variety, we propose exploiting naïve behavior to map the neuronal identity. Here we investigate the feasibility of identifying neurons "ecologically" using their activation for natural behavioral and environmental parameters. Such a neuronal ECO-marker might give a finer granularity than possible with genetic or molecular markers, thereby facilitating the comparison of the functional characteristics of individual neurons across animals. In contrast to a potential mapping using artificial stimuli and trained behavior which have an unlimited parameter space, an ecological mapping is experimentally feasible since it is bounded by the ecology. Home-cage environment is an excellent basis for this ECO-mapping covering an extensive behavioral repertoire and since home-cage behavior is similar across laboratories. We review the possibility of adding area-specific environmental enrichment and automatized behavioral tasks to identify neurons in specific brain areas. In this work, we focus on the visual cortex, motor cortex, prefrontal cortex, and hippocampus. Fundamental to achieving this identification is to take advantage of state-of-the-art behavioral tracking, sensory stimulation protocols, and the plethora of creative behavioral solutions for rodents. We find that motor areas might be easiest to address, followed by prefrontal, hippocampal, and visual areas. The possibility of acquiring a near-complete ecological identification with minimal animal handling, minimal constraints on the main experiment, and data compatibility across laboratories might outweigh the necessity of implanting electrodes or imaging devices.

q-bio.NC

Open-Source Tools for Behavioral Video Analysis: Setup, Methods, and Development

Recently developed methods for video analysis, especially models for pose estimation and behavior classification, are transforming behavioral quantification to be more precise, scalable, and reproducible in fields such as neuroscience and ethology. These tools overcome long-standing limitations of manual scoring of video frames and traditional "center of mass" tracking algorithms to enable video analysis at scale. The expansion of open-source tools for video acquisition and analysis has led to new experimental approaches to understand behavior. Here, we review currently available open-source tools for video analysis and discuss how to set up these methods for labs new to video recording. We also discuss best practices for developing and using video analysis methods, including community-wide standards and critical needs for the open sharing of datasets and code, more widespread comparisons of video analysis methods, and better documentation for these methods especially for new users. We encourage broader adoption and continued development of these tools, which have tremendous potential for accelerating scientific progress in understanding the brain and behavior.

q-bio.QM