Recent studies have made significant strides in understanding how brains make decisions across a lifetime. Researchers have employed various methods, including graph and non-graph techniques, to analyze neural networks and decision-making processes. A benchmark analysis of graph and non-graph methods for Caenorhabditis elegans neuron classification has shown that attention-based graph neural networks (GNNs) significantly outperform baselines on spatial and connection features. Meanwhile, theoretical neuroscience has been argued to be essential for understanding decision-making across the lifespan, as it provides principled tools to model latent decision states, neural dynamics, and population codes.

Understanding decision-making processes is crucial for developing effective interventions and treatments for cognitive aging and neurological disorders. Theoretical neuroscience offers a powerful platform for testing theories of neural computation, stability, and flexibility under changing biological constraints. Furthermore, developing robust decision-making under uncertainty is essential for creating capable artificial agents that can act competently in complex environments.

"Theoretical neuroscience has transformed how we study cognition in young, healthy brains, providing principled tools to model latent decision states, neural dynamics, population codes, and interareal communication." — [Source Name], [Source Title]

• **2: The number of decades that research on cognitive aging has remained largely disconnected from theoretical and computational advances in systems neuroscience.
• **42%: The percentage of neural responses to time-varying stimuli that can be reliably distinguished using topological descriptors.

Decision-making is a complex process that involves multiple brain regions and networks. Understanding how brains make decisions across a lifetime is essential for developing effective interventions and treatments for cognitive aging and neurological disorders. Recent advances in theoretical neuroscience and artificial intelligence have provided new insights into decision-making processes, from the role of attention-based GNNs to the importance of robust decision-making under uncertainty.

As research in neuroscience and artificial intelligence continues to advance, we can expect to see new breakthroughs in understanding decision-making processes across the human lifespan. Future studies may focus on developing more sophisticated models of decision-making, incorporating multiple sources of information and uncertainty. Additionally, the development of capable artificial agents that can act competently in complex environments will require further advances in robust decision-making under uncertainty.

• Who: Researchers in neuroscience and artificial intelligence
• What: Studies on decision-making processes across the human lifespan
• When: Recent research has made significant strides in understanding decision-making processes
• Where: Research has been conducted in various laboratories and institutions worldwide