Decoding How Humans Encode Memories
Description
Advancements in closed-loop deep brain stimulation (DBS) enabled more intelligent autonomy for therapeutic intervention across a wide range of neurologic and psychiatric disorders. The predominant approach relies on control-theoretic approximations of the brain’s complex functional relationships with the external environment–in particular, a mapping between targeted stimulation and naturalistic responses of different regions of the brain. However, existing approaches fail to capture the environmental context of neuronal biomarkers. Thus, we leverage a set of IoT sensors to capture the human experience and environmental context, i.e., a subset of human sensory channels, in order to estimate the state of the human brain and provide the foundation for smarter, context-dependent DBS. We explore neural-symbolic approaches that integrate the powerful perception capabilities of deep learning with human logic to reason about the complex dependencies across a heterogeneous set of sensors.
Advisors
Skills Required by the team
- Python
- Deep Learning