The Speakers

A machine learning-enabled platform for generation of insights into neurological disease

We have developed an advanced platform for modeling neurological diseases that integrates iPSC-based disease modeling, functional genomics, and large-scale automation. Machine learning powers the entire platform, driving deeper insights into disease biology, as well as target and drug discovery. In this presentation, we will share several examples of our progress, focusing on discoveries related to Tuberous Sclerosis (TSC) and ALS. We will highlight our use of optical pooled screening and perturb-seq for target identification and generating biological insights. Additionally, we will discuss the data pipelines and production-ready machine learning models that are essential for unraveling the complexities of neurological diseases.

A less artificial intelligence

‘You … your memories and ambitions, your sense of personal identity and free will, are in fact no more than the behavior of a vast assembly of nerve cells …’ Crick’s words capture the profound challenge of decrypting the neural code. This challenge has long been hindered by our limited ability to record activity from large neuronal populations under the complex, variable conditions in which brains evolve, and our capacity to model the intricate relationships between stimuli, behaviors, and neural activity.

Recent breakthroughs are starting to overcome these barriers. Cutting-edge technologies now enable large-scale recordings, while AI can construct predictive brain models that link stimuli, neural activity, and behavior. These digital twins open the door to limitless in silico experiments, testing theories that are otherwise impossible at scale in living brains. I will discuss our groundbreaking work in creating these digital twins and uncovering neural representation mechanisms, which we validate with closed-loop experiments.