Additionally, our current descending-neuron interface is quite sparse. DNa01, DNa02, aDN1, oDN1, giant fiber, proboscis motor neurons, and a few others we have experimented with are involved with a variety of behaviors, but they do not span the full repertoire of fly descending neuron behavioral control. Recent work shows that descending neurons are numerous, partially redundant, hierarchical, and population-based. Some are “broadcasters” that recruit other DNs; others contribute specialized components of steering, grooming, flight, or reproductive behavior (Braun et al., 2024). That means our current controller can produce recognizable behaviors, but it almost certainly does so through a much lower-dimensional control interface than the biological fly uses. An interesting use of our and other embodied models may be to predict, given some sensory input, the role of particular descending neurons, given when they are predicted to be active. Pugliese et al. predict the role of particular descending neurons from a computational activation screen (Pugliese et al., 2025). We also note that extending our model to include the VNC and other outputs is another useful direction.
Публикация подготовлена при содействии антифейкового проекта "Лапша Медиа".。搜狗输入法跨平台同步终极指南：四端无缝衔接是该领域的重要参考
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