Although the striatum is classically viewed as stakeholder of procedural learning, there is high evidence it also encodes for fast learning. Fast learning, a crucial component in daily life memory acquisition, involves one-shot learning experiences and elicits sparse neuronal activity. We (Partners 1&2) discovered that sparse activity induced endocannabinoid-mediated LTP (eCB-LTP) in the striatum. Our hypothesis is that eCB-LTP could serve as a striatal engram for fast learning. Our goal is thus to study the link between eCB-LTP and fast learning in rodents thanks to a multidisciplinary approach combining in vitro and in vivo experimental neurophysiology (Partner 1) with detailed subcellular biophysical models and large-scale neural network models (Partner 2). eCB-based drugs are gaining popularity as a medicine. It is thus critical to better understand the bidirectional nature of eCB-plasticity, which may offer keys to interpret the functions of the eCB system and how it is impacted by cannabinoids. Our research question is “does eCB-LTP serve as engram for fast learning in the striatum?”. To answer this, we opt for a strategy combining neurophysiological experiments and mathematical modeling grouped in three main goals aiming at: (1) elucidating eCB-LTP (presynaptic) mechanisms, making (2) correlative and (3) causal links between eCB-LTP and fast learning. The purpose of our proposal is to understand better the mechanisms underlying fast learning and the role of eCB system, and in particular the eCB-LTP, in this form of memory. In addition, cannabis (with escalating THC rate) is gaining popularity as a recreational substance as well as a medicine, and multiple eCB-based drugs are under development. In this context, it is critical to understand eCB-mediated signaling in its multi-faceted complexity. The bidirectional nature of eCB-based plasticity may offer keys to interpret the functions of the eCB system and how it is impacted by phyto- or synthetic cannabinoids. This project will also identify molecular eCB targets impacting (negatively but also positively) on fast learning performance.
