Identifying super-spreaders can be framed as a subtask of the influence maximisation problem. It seeks to pinpoint agents within a network that, if selected as single diffusion seeds, disseminate information most effectively. Multilayer networks, a specific class of heterogeneous graphs, can capture diverse types of interactions (e.g., physical-virtual or professional-social), and thus offer a more accurate representation of complex relational structures. In this work, we introduce a novel approach to identifying super-spreaders in such networks by leveraging graph neural networks. To this end, we construct a dataset by simulating information diffusion across hundreds of networks - to the best of our knowledge, the first of its kind tailored specifically to multilayer networks. We further formulate the task as a variation of the ranking prediction problem based on a four-dimensional vector that quantifies each agent's spreading potential: (i) the number of activations; (ii) the duration of the diffusion process; (iii) the peak number of activations; and (iv) the simulation step at which this peak occurs. Our model, TopSpreadersNetwork, comprises a relationship-agnostic encoder and a custom aggregation layer. This design enables generalisation to previously unseen data and adapts to varying graph sizes. In an extensive evaluation, we compare our model against classic centrality-based heuristics and competitive deep learning methods. The results, obtained across a broad spectrum of real-world and synthetic multilayer networks, demonstrate that TopSpreadersNetwork achieves superior performance in identifying high-impact nodes, while also offering improved interpretability through its structured output.

Throughout history, the development of artificial intelligence, particularly artificial neural networks, has been open to and constantly inspired by the increasingly deepened understanding of the brain, such as the inspiration of neocognitron, which is the pioneering work of convolutional neural networks. Per the motives of the emerging field: NeuroAI, a great amount of neuroscience knowledge can help catalyze the next generation of AI by endowing a network with more powerful capabilities. As we know, the human brain has numerous morphologically and functionally different neurons, while artificial neural networks are almost exclusively built on a single neuron type. In the human brain, neuronal diversity is an enabling factor for all kinds of biological intelligent behaviors. Since an artificial network is a miniature of the human brain, introducing neuronal diversity should be valuable in terms of addressing those essential problems of artificial networks such as efficiency, interpretability, and memory. In this Primer, we first discuss the preliminaries of biological neuronal diversity and the characteristics of information transmission and processing in a biological neuron. Then, we review studies of designing new neurons for artificial networks. Next, we discuss what gains can neuronal diversity bring into artificial networks and exemplary applications in several important fields. Lastly, we discuss the challenges and future directions of neuronal diversity to explore the potential of NeuroAI.

One of the key problems in computer vision is adaptation: models are too rigid to follow the variability of the inputs. The canonical computation that explains adaptation in sensory neuroscience is divisive normalization, and it has appealing effects on image manifolds. In this work we show that including divisive normalization in current deep networks makes them more invariant to non-informative changes in the images. In particular, we focus on U-Net architectures for image segmentation. Experiments show that the inclusion of divisive normalization in the U-Net architecture leads to better segmentation results with respect to conventional U-Net. The gain increases steadily when dealing with images acquired in bad weather conditions. In addition to the results on the Cityscapes and Foggy Cityscapes datasets, we explain these advantages through visualization of the responses: the equalization induced by the divisive normalization leads to more invariant features to local changes in contrast and illumination.

When artificial intelligence is tasked with visually identifying objects and faces, it assigns specific components of its network to face recognition -- just like the human brain. The human brain seems to care a lot about faces. It's dedicated a specific area to identifying them, and the neurons there are so good at their job that most of us can readily recognize thousands of individuals. With artificial intelligence, computers can now recognize faces with a similar efficiency -- and neuroscientists at MIT's McGovern Institute for Brain Research have found that a computational network trained to identify faces and other objects discovers a surprisingly brain-like strategy to sort them all out. The finding, reported on March 16, 2022, in Science Advances, suggests that the millions of years of evolution that have shaped circuits in the human brain have optimized our system for facial recognition.

The human brain seems to care a lot about faces. It's dedicated a specific area to identifying them, and the neurons there are so good at their job that most of us can readily recognize thousands of individuals. With artificial intelligence, computers can now recognize faces with a similar efficiency -- and neuroscientists at MIT's McGovern Institute for Brain Research have found that a computational network trained to identify faces and other objects discovers a surprisingly brain-like strategy to sort them all out. The finding, reported March 16 in Science Advances, suggests that the millions of years of evolution that have shaped circuits in the human brain have optimized our system for facial recognition. "The human brain's solution is to segregate the processing of faces from the processing of objects," explains Katharina Dobs, who led the study as a postdoc in the lab of McGovern investigator Nancy Kanwisher, the Walter A. Rosenblith Professor of Cognitive Neuroscience at MIT.

Artificial neural networks modeled on real brains can perform cognitive tasks. A new study shows that artificial intelligence networks based on human brain connectivity can perform cognitive tasks efficiently. By examining MRI data from a large Open Science repository, researchers reconstructed a brain connectivity pattern, and applied it to an artificial neural network (ANN). An ANN is a computing system consisting of multiple input and output units, much like the biological brain. A team of researchers from The Neuro (Montreal Neurological Institute-Hospital) and the Quebec Artificial Intelligence Institute trained the ANN to perform a cognitive memory task and observed how it worked to complete the assignment.

Summary: Artificial neural networks modeled on human brain connectivity can effectively perform complex cognitive tasks. A new study shows that artificial intelligence networks based on human brain connectivity can perform cognitive tasks efficiently. By examining MRI data from a large Open Science repository, researchers reconstructed a brain connectivity pattern, and applied it to an artificial neural network (ANN). An ANN is a computing system consisting of multiple input and output units, much like the biological brain. A team of researchers from The Neuro (Montreal Neurological Institute-Hospital) and the Quebec Artificial Intelligence Institute trained the ANN to perform a cognitive memory task and observed how it worked to complete the assignment.

While the parallels between branch specialization in artificial neural networks and neural circuits in the brain are striking, there are clearly major differences and many outstanding questions. From the perspective of building artificial neural networks, we wonder if branch specific tuning of individual units and their connectivity rules would enhance performance? In the brain, there is good evidence that the activation functions of individual neurons are fine-tuned between and even within distinct neural circuits. If this fine tuning confers benefits to the brain then we might expect similar benefits in artificial networks. From the perspective of understanding the brain, we wonder whether branch specialisation could help make experimentally testable predictions?

Every single one of your thoughts is made possible by your biological neurons. And behind many of the most useful A.I architectures is an entity inspired by them. Neurons are at the epicenter of the processing that underpins the complexity produced by intelligent systems. Curious to know more about the engine of your thoughts and about how they compare to their artificial counterparts? A.I neurons were originally inspired by our biological ones, yet they are very different.

Every day we think, reason, communicate with each other and this is normal. After all, we are sentient beings. But now in this world, not only us have the ability to think, but also yet another human creation – artificial intelligence (AI). So, what is AI? Artificial intelligence has enabled computers to learn with the help of a teacher as well as from their own experience. Neural networks can quickly adapt to the enormous volume of new parameters and perform tasks that they couldn't handle before.