Meta-learning local synaptic plasticity for continual familiarity detection

Over the course of a lifetime, a continual stream of information is encoded and retrieved from memory. To explore the synaptic mechanisms that enable this ongoing process, we consider a continual familiarity detection task in which a subject must report whether an image has been previously encountered. We design a class of feedforward neural network models endowed with biologically plausible synaptic plasticity dynamics, the parameters of which are meta-learned to optimize familiarity detection over long delay intervals. After training, we find that anti-Hebbian plasticity leads to better performance than Hebbian and replicates experimental results from the inferotemporal cortex, including repetition suppression. Unlike previous models, this network both operates continuously without requiring any synaptic resets and generalizes to intervals it has not been trained on. We demonstrate this not only for uncorrelated random stimuli but also for images of real-world objects. Our work suggests a biologically plausible mechanism for continual learning, and demonstrates an effective application of machine learning for neuroscience discovery.