The current source text is the original text and can be switched to the Chinese translation.
SaveShare
Overview
arXiv:2611.06940v1 Announce Type: cross Abstract: The public release of the complete adult fruit-fly brain connectome enables brain-scale spiking-network studies, motivating closed-loop assessment of connection loss. We test a connectome-derived controller in synthetic pursuit-escape and check record consistency. Across 576 test episodes in paired campaigns, the baseline mapping at 50% connection-row deletion yields 11/48 proportional and 0/48 relay collisions by 1 s, despite paired P/R trigger/burst schedules. The prespecified secondary relay-minus-proportional net-loss difference is -22.9 percentage points (95% seed-cluster CI [-33.3, -12.5]). Two separately recalibrated mappings yield smaller, uncertain point estimates; unequal healthy clearance, recalibration and different test cohorts limit attribution. A frozen command-rate warning detects only 2/13 baseline collisions across all tested deletion levels. In a separate author-designed fly/delayed-tank catalogue, checks plus model-specific replay detect 81/87 corruptions versus 36/87 for trusted headers, accepting all 20 distinct valid inputs (six misses). These results quantify finite-horizon collisions and the gains and limits of record checks.
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs .
READ THE ORIGINAL
Read the original for the full context.
The content of this article comes from arXiv cs.NE,Copyright belongs to the original author and publisher.
arXiv:2611.08645v1 Announce Type: new Abstract: Understanding building facades is central to urban digital twins, heritage modelling, and sustainable city analysis, yet existing LiDAR benchmarks stop at geometry and semantic segmentation and offer little support for reasoning about the functional role of each architectural element.
arXiv:2611.09295v1 Announce Type: new Abstract: Transforming the spike trains produced by spiking neural networks (SNNs) into continuous signals through biologically inspired mechanisms remains an open challenge in generative and regression tasks. In biological systems, a similar transformation occurs when muscles integrate neural spikes to produce smooth, coordinated movements.
arXiv:2611.08730v1 Announce Type: new Abstract: LLM-based digital twins aim to predict how individuals would respond in new settings from representations of their prior responses. Existing evidence that compression of survey histories need not reduce predictive accuracy suggests that representation structure, rather than information volume alone, may be an important design choice.
Loading comments…