One of the long-term goals of artificial intelligence (AI) is to build machines that can continually learn new knowledge from their experiences, ground these experiences in the physical world, and ...

Faster and Simpler: The code is greatly simplified and refactored in Python/PyTorch. We also replaced the simulator with our CUDA-accelerated randomized environment, which is faster, lightweight, and ...

As textual reasoning with large language models (LLMs) has advanced significantly, there has been growing interest in enhancing the multimodal reasoning capabilities of large vision-language models ...

Plasticity of synapses in the brain is responsible for memory, learning, and intelligence. Consequently, emulating aspects of biological synaptic plasticity is a fundamental goal in electrical ...

Generating synthetic locomotory and neural data is a useful yet cumbersome step commonly required to study theoretical models of the brain’s role in spatial navigation. This process can be time ...

The basal ganglia (BG) contribute to reinforcement learning (RL) and decision-making, but unlike artificial RL agents, it relies on complex circuitry and dynamic dopamine modulation of opponent ...

Not having access to compact and meaningful representations is known to significantly increase the complexity of reinforcement learning (RL). For this reason, it can be useful to perform state ...