Despite the progress in deep learning networks, efficient learning at the
edge (enabling adaptable, low-complexity machine learning solutions) remains a
critical need for defense and commercial applications. We envision a pipeline
to utilize large neuroimaging datasets, including maps of the brain which
capture neuron and synapse connectivity, to improve machine learning
approaches. We have pursued different approaches within this pipeline
structure. First, as a demonstration of data-driven discovery, the team has
developed a technique for discovery of repeated subcircuits, or motifs. These
were incorporated into a neural architecture search approach to evolve network
architectures. Second, we have conducted analysis of the heading direction
circuit in the fruit fly, which performs fusion of visual and angular velocity
features, to explore augmenting existing computational models with new insight.
Our team discovered a novel pattern of connectivity, implemented a new model,
and demonstrated sensor fusion on a robotic platform. Third, the team analyzed
circuitry for memory formation in the fruit fly connectome, enabling the design
of a novel generative replay approach. Finally, the team has begun analysis of
connectivity in mammalian cortex to explore potential improvements to
transformer networks. These constraints increased network robustness on the
most challenging examples in the CIFAR-10-C computer vision robustness
benchmark task, while reducing learnable attention parameters by over an order
of magnitude. Taken together, these results demonstrate multiple potential
approaches to utilize insight from neural systems for developing robust and
efficient machine learning techniques.

利用大型神经成像数据集中的连接性图谱等手段，改善边缘端机器学习的效率和鲁棒性，并通过对果蝇和哺乳动物视脑回路连接等领域的分析，展示了多种潜在的应用脑科学领域洞见的方法。