Classic supervised learning models posit that, when two stimuli predict similar outcomes, their representations integrate. However, these models have recently been challenged by studies showing that pairing stimuli with a shared associate can sometimes cause differentiation. (2/5)Classic supervised learning models posit that, when two stimuli predict similar outcomes, their representations integrate. However, these models have recently been challenged by studies showing that pairing stimuli with a shared associate can sometimes cause differentiation. (2/5)

We provide an unsupervised NN model that can explain these and other related findings by implementing the Nonmonotonic Plasticity Hypothesis (NMPH), whereby moderate coactivity weakens weights, leading to differentiation, and strong coactivity strengthens weights, leading to integration (3/5)We provide an unsupervised NN model that can explain these and other related findings by implementing the Nonmonotonic Plasticity Hypothesis (NMPH), whereby moderate coactivity weakens weights, leading to differentiation, and strong coactivity strengthens weights, leading to integration (3/5)