PRE2023-Reinforcement learning as a path to understanding declarative memories

Understanding the cognitive process of memory not only has an enormous impact on the study of brain function but also contributes to the worldwide fight for the prevention, diagnosis, and treatment of mental diseases that affect memory. In 2020 there were 55 million people worldwide living with dementia, including Alzheimer's disease. This number will almost double every 20 years, reaching 78 million in 2030 and 139 million in 2050 due to a globally aging population. Many neurodevelopmental disorders display severe learning and memory deficits as part of their symptoms. Understanding memory is crucial for developing diagnostic tests, to ameliorate symptoms and personalize treatments. Despite the long history of studying long-term memory, its circuit mechanisms are still vaguely understood. The complexity of this scientific question lies in the large number of neurons and synapses that can potentially participate in memory storage. In our lab, we believe that neuroscience can take advantage of modern algorithms in AI to understand how the brain works. For this reason, we are seeking the contribution of young investigators with a degree or master’s in neuroscience, physics, applied math, bioinformatics, or similar backgrounds to participate in our endeavor to understand the principles behind long-term memory. TFM students will learn animal behavior, neuronal activity recordings, and data analysis during, and contribute to different aspects of the projects in our lab.