Improving Speed and Cost of Entry for Holographic Optogenetics

PROJECT SUMMARY/ABSTRACT Mapping the neural networks in the human brain will help researchers shed light on neurological diseases such as Alzheimer’s and Schizophrenia, as well as the workings of memory, vision, and consciousness. When first demonstrated over 15 years ago, holographic photostimulation of 3D populations of neurons represented a transformative innovation in the way that functional brain mapping was conducted in neuroscience. In this approach, neuroscientists use Spatial Light Modulators (SLMs) to holographically project points of light into living brains. Photostimulating precisely targeted neuronal ensembles in this way more closely mimics natural brain activity to significantly improve functional mapping capabilties. Through the hard work of early holographic neuroscience trailblazers and SLM manufacturers, this approach has now become widely adopted. Now through the combination of fast voltage indicators and opsins, high-speed SLMs, efficient data analysis, and rapid data pipelines, neuroscientists are pushing toward the goal of performing closed-loop experiments, wherein the optical control system can intervene in real-time in response to observed neural activity or behavior. Closing the loop and enabling activity-guided control of neural circuit dynamics will provide neuroscientists a powerful new tool to establish causal relationships in circuit architecture and tune optogenetic interventions for optimum response. To realize this goal, SLMs need to not only provide sub-millisecond liquid crystal response, but also a fast data pipeline that doesn’t rely on pre-calculated and stored data. In our prior work, we have demonstrated that such capabilities are technically achievable, however current solutions are not commercially viable due to prohibitively high costs and supply chain limitations. The proposed work will develop new SLM hardware and firmware to address these requirements, while leveraging a new modular architecture to provide cost-effective SLMs that will be affordable to the neuroscience community.