Neuromorphic computing uses brain-inspired principles to design circuits that can perform
computational tasks with superior power efficiency to conventional computers. Approaches …

The human brain is a sophisticated, high‐performance biocomputer that processes multiple
complex tasks in parallel with high efficiency and remarkably low power consumption …

Despite rapid adoption and deployment of large language models (LLMs), the internal
computations of these models remain opaque and poorly understood. In this work, we seek …

Contextual embeddings, derived from deep language models (DLMs), provide a continuous
vectorial representation of language. This embedding space differs fundamentally from the …

Nanowire networks (NWNs) mimic the brain's neurosynaptic connectivity and emergent
dynamics. Consequently, NWNs may also emulate the synaptic processes that enable …

Artificial neuromorphic systems based on populations of spiking neurons are an
indispensable tool in understanding the human brain and in constructing neuromimetic …

Brain-machine interfaces (BMIs) combine methods, approaches, and concepts derived from
neurophysiology, computer science, and engineering in an effort to establish real-time …

The neurovisceral integration (NVI) model was originally proposed to account for observed
relationships between peripheral physiology, cognitive performance, and emotional/physical …

Social interactions involve complex decision-making tasks that are shaped by dynamic,
mutual feedback between participants. An open question is whether and how emergent …

Spontaneous and sensory-evoked cortical activity is highly state-dependent, yet relatively
little is known about transitions between distinct waking states. Patterns of activity in mouse …