Cebra for Web Apps

CEBRA, or Learnable Latent Embeddings for Joint Behavioural and Neural Analysis, is a sophisticated machine-learning tool aimed at integrating behavioral actions with neural activity, a key aspect of neuroscience research. This program excels in creating high-performance latent spaces that elucidate the connections between behavior and neural correlates. It is adept at processing both single and multi-session datasets, accommodating calcium and electrophysiology data, and offers capabilities such as decoding natural movies from the visual cortex and reconstructing viewed videos from the mouse brain.

The key features of CEBRA include space mapping, kinematic feature analysis, and rapid decoding, making it a significant resource for researchers in neuroscience and behavioral science. Its versatility in handling various data types enhances its applicability across different studies. CEBRA’s design focuses on facilitating deeper insights into the neural mechanisms underlying behavior, thus proving to be an essential tool for advancing scientific understanding in these fields.