【专题研究】Magnetic g是当前备受关注的重要议题。本报告综合多方权威数据，深入剖析行业现状与未来走向。
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.
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