Deep neural networks (DNNs) are machine learning algorithms that have revolutionized
computer vision due to their remarkable successes in tasks like object classification and …

Since the beginning of the 21st century, there is no doubt that the importance of artificial
intelligence has been highlighted in many fields, among which the memristor-based artificial …

Humans have the remarkable cognitive capacity to rapidly adapt to changing environments.
Central to this capacity is the ability to form high-level, abstract representations that take …

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

The brain is a complex system comprising a myriad of interacting neurons, posing significant
challenges in understanding its structure, function, and dynamics. Network science has …

Realizing high performance of ordinary robots is one of the core problems in robotic
research. Improving the performance of ordinary robots usually relies on the collaborative …

Modular and distributed coding theories of category selectivity along the human ventral
visual stream have long existed in tension. Here, we present a reconciling framework …

Animals likely use a variety of strategies to solve laboratory tasks. Traditionally, combined
analysis of behavioral and neural recording data across subjects employing different …

The reactivation of experience-based neural activity patterns in the hippocampus is crucial
for learning and memory. These reactivation patterns and their associated sharp-wave …

The ability to abstract information to guide decisions during navigation across changing
environments is essential for adaptation and requires the integrity of the hippocampal …