James B Aimone

Anthony Zador, Jean-Marc Fellous, Terrence Sejnowski et al. · uw

Neuroscience and Artificial Intelligence (AI) have made impressive progress in recent years but remain only loosely interconnected. Based on a workshop convened by the National Science Foundation in August 2025, we identify three fundamental capability gaps in current AI: the inability to interact with the physical world, inadequate learning that produces brittle systems, and unsustainable energy and data inefficiency. We describe the neuroscience principles that address each: co-design of body and controller, prediction through interaction, multi-scale learning with neuromodulatory control, hierarchical distributed architectures, and sparse event-driven computation. We present a research roadmap organized around these principles at near, mid, and long-term horizons. We argue that realizing this program requires a new generation of researchers trained across the boundary between neuroscience and engineering, and describe the institutional conditions: interdisciplinary training, hardware access, community standards, and ethics, needed to support them. We conclude that NeuroAI, neuroscience-informed artificial intelligence, has the potential to overcome limitations of current AI while deepening our understanding of biological neural computation.

James B Aimone

Neuromorphic computing (NMC) is increasingly viewed as a low-power alternative to conventional von Neumann architectures such as central processing units (CPUs) and graphics processing units (GPUs), however the computational value proposition has been difficult to define precisely. Here, we propose a computational framework for analyzing NMC algorithms and architectures. Using this framework, we demonstrate that NMC can be analyzed as general-purpose and programmable even though it differs considerably from a conventional stored-program architecture. We show that the time and space scaling of idealized NMC has comparable time and footprint tradeoffs that align with that of a theoretically infinite processor conventional system. In contrast, energy scaling for NMC is significantly different than conventional systems, as NMC energy costs are event-driven. Using this framework, we show that while energy in conventional systems is largely determined by the scheduled operations determined by the structural algorithm graph, the energy of neuromorphic systems scales with the activity of the algorithm, that is the activity trace of the algorithm graph. Without making strong assumptions on NMC or conventional costs, we demonstrate which neuromorphic algorithm formulations can exhibit asymptotically improved energy scaling when activity is sparse and decaying over time. We further use these results to identify which broad algorithm families are more or less suitable for NMC approaches.

James B Aimone, William Severa, Craig M Vineyard

Neuromorphic hardware architectures represent a growing family of potential post-Moore's Law Era platforms. Largely due to event-driving processing inspired by the human brain, these computer platforms can offer significant energy benefits compared to traditional von Neumann processors. Unfortunately there still remains considerable difficulty in successfully programming, configuring and deploying neuromorphic systems. We present the Fugu framework as an answer to this need. Rather than necessitating a developer attain intricate knowledge of how to program and exploit spiking neural dynamics to utilize the potential benefits of neuromorphic computing, Fugu is designed to provide a higher level abstraction as a hardware-independent mechanism for linking a variety of scalable spiking neural algorithms from a variety of sources. Individual kernels linked together provide sophisticated processing through compositionality. Fugu is intended to be suitable for a wide-range of neuromorphic applications, including machine learning, scientific computing, and more brain-inspired neural algorithms. Ultimately, we hope the community adopts this and other open standardization attempts allowing for free exchange and easy implementations of the ever-growing list of spiking neural algorithms.