Topic: RNNs

Rajanlab designs neural network models constrained by experimental data, and reverse engineers them to figure out how brain circuits function in health and disease. Recently, we have been developing a powerful new theory-based framework for “in-vivo tract tracing” from multi-regional neural activity collected experimentally. We call this framework CURrent-Based Decomposition (CURBD). CURBD employs recurrent neural networks (RNNs) directly constrained, from the outset, by time series measurements acquired experimentally, such as Ca2+ imaging or electrophysiological data. Once trained, these dat

Animals continuously process information from multiple sensory modalities to assess their environment and guide their behavior. For example, a predator may rely on both visual and auditory cues to track its prey. Over time, various algorithms have been developed to describe multisensory processing, including linear and nonlinear fusion. However, these algorithms typically treat each time step independently and fail to account for temporal dependencies in these signals. To address this limitation, we introduce a novel set of multisensory tasks that systematically incorporate controlled temporal