Topology shapes dynamics of higher-order networks
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Land use conversion increases network complexity and stability of soil microbial communities in a temperate grassland
Soils harbor highly diverse microbial communities that are critical to soil health, but agriculture has caused extensive land use conversion resulting in negative effects on critical ecosystem processes. However, the responses and adaptations of microbial communities to land use conversion have not yet been understood. Here, we examined the effects of land conversion for long-term crop use on the network complexity and stability of soil microbial communities over 19 months. Despite reduced microbial biodiversity in comparison with native tallgrass prairie, conventionally tilled (CT) cropland significantly increased network complexity such as connectivity, connectance, average clustering coefficient, relative modularity, and the number of species acting at network hubs and connectors as well as resulted in greater temporal variation of complexity indices. Molecular ecological networks under CT cropland became significantly more robust and less vulnerable, overall increasing network stability. The relationship between network complexity and stability was also substantially strengthened due to land use conversion. Lastly, CT cropland decreased the number of relationships between network structure and environmental properties instead being strongly correlated to management disturbances. These results indicate that agricultural disturbance generally increases the complexity and stability of species “interactions”, possibly as a trade-off for biodiversity loss to support ecosystem function when faced with frequent agricultural disturbance.
Configural processing as an optimized strategy for robust object recognition in neural networks
Configural processing, the perception of spatial relationships among an object’s components, is crucial for object recognition, yet its teleology and underlying mechanisms remain unclear. We hypothesize that configural processing drives robust recognition under varying conditions. Using identification tasks with composite letter stimuli, we compare neural network models trained with either configural or local cues. We find that configural cues support robust generalization across geometric transformations (e.g., rotation, scaling) and novel feature sets. When both cues are available, configural cues dominate local features. Layerwise analysis reveals that sensitivity to configural cues emerges later in processing, likely enhancing robustness to pixel-level transformations. Notably, this occurs in a purely feedforward manner without recurrent computations. These findings with letter stimuli successfully extend to naturalistic face images. Our results demonstrate that configural processing emerges in a naíve network based on task contingencies, and is beneficial for robust object processing under varying viewing conditions.
Grid-enhancing technologies for clean energy systems
Renewable energy source integration into energy systems can contribute to transmission congestion, which requires time-consuming and capital-intensive upgrades to address. Grid-enhancing technologies (GETs) can increase the capacity of grids with minimal investment, preventing congestion and curtailment of renewable energy. In this Review, we discuss the principles and uses of GETs, which use software and/or hardware to interpret real-time conditions to better use the existing capacity of grid assets. GETs include dynamic line ratings, dynamic transformer ratings, power flow controls, topology optimization, advanced conductor technologies, energy storage systems, and demand response. These GETs can enhance system performance individually, but the deployment of multiple GETs together would greatly increase their effect on the grid capacity and stability by removing multiple capacity bottlenecks in parallel. Infrastructure for real-time data acquisition, transmission and analysis is key to successfully deploying GETs but requires further development and commercialization for broader deployment.
MARBLE: interpretable representations of neural population dynamics using geometric deep learning
The dynamics of neuron populations commonly evolve on low-dimensional manifolds. Thus, we need methods that learn the dynamical processes over neural manifolds to infer interpretable and consistent latent representations. We introduce a representation learning method, MARBLE, which decomposes on-manifold dynamics into local flow fields and maps them into a common latent space using unsupervised geometric deep learning. In simulated nonlinear dynamical systems, recurrent neural networks and experimental single-neuron recordings from primates and rodents, we discover emergent low-dimensional latent representations that parametrize high-dimensional neural dynamics during gain modulation, decision-making and changes in the internal state. These representations are consistent across neural networks and animals, enabling the robust comparison of cognitive computations. Extensive benchmarking demonstrates state-of-the-art within- and across-animal decoding accuracy of MARBLE compared to current representation learning approaches, with minimal user input. Our results suggest that a manifold structure provides a powerful inductive bias to develop decoding algorithms and assimilate data across experiments.
Human neural dynamics of real-world and imagined navigation
The ability to form episodic memories and later imagine them is integral to the human experience, influencing our recollection of the past and envisioning of the future. While rodent studies suggest the medial temporal lobe, especially the hippocampus, is involved in these functions, its role in human imagination remains uncertain. In human participants, imaginations can be explicitly instructed and reported. Here we investigate hippocampal theta oscillations during real-world and imagined navigation using motion capture and intracranial electroencephalographic recordings from individuals with chronically implanted medial temporal lobe electrodes. Our results revealed intermittent theta dynamics, particularly within the hippocampus, encoding spatial information and partitioning navigational routes into linear segments during real-world navigation. During imagined navigation, theta dynamics exhibited similar patterns despite the absence of external cues. A statistical model successfully reconstructed real-world and imagined positions, providing insights into the neural mechanisms underlying human navigation and imagination, with implications for understanding memory in real-world settings.
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