Abstract
Studies of hippocampal learning have obtained seemingly contradictory results, with manipulations that increase coactivation of memories sometimes leading to differentiation of these memories, but sometimes not. These results could potentially be reconciled using the nonmonotonic plasticity hypothesis, which posits that representational change (memories moving apart or together) is a U-shaped function of the coactivation of these memories during learning. Testing this hypothesis requires manipulating coactivation over a wide enough range to reveal the full U-shape. To accomplish this, we used a novel neural network image synthesis procedure to create pairs of stimuli that varied parametrically in their similarity in high-level visual regions that provide input to the hippocampus. Sequences of these pairs were shown to human participants during high-resolution fMRI. As predicted, learning changed the representations of paired images in the dentate gyrus as a U-shaped function of image similarity, with neural differentiation occurring only for moderately similar images.
Highlights
Humans constantly learn new facts, encounter new events, and see and hear new things
Before looking at the effects of statistical learning on hippocampal representations, we wanted to verify that our model-based synthesis approach was effective in creating graded levels of feature similarity in the targeted layers of the network: our goal was to synthesize images that varied parametrically in their similarity in higher layers while not differing systematically in lower and middle layers of the network
Future work could monitor the time course of representational change, either by interleaving additional templating runs throughout statistical learning, or by exploiting methods with higher temporal resolution where the responses to stimuli presented close in time can more readily be disentangled. These results highlight the complexity of learning rules in the hippocampus, showing that in dentate gyrus (DG), moderate levels of visual feature similarity lead to differentiation following a statistical learning paradigm, but higher and lower levels of visual similarity do not
Summary
Humans constantly learn new facts, encounter new events, and see and hear new things. Managing this incoming information requires accommodating the new with the old, reorganizing memory as we learn from experience. Coactivation of neurons leads to strengthened connections between these neurons This logic can scale up to the level of many synapses among entire populations of neurons, comprising distributed representations. A greater degree of coactivation among representations will strengthen shared connections and lead to integration. Consistent with this view, arbitrary pairs of objects integrate in the hippocampus following repeated temporal or spatial co-occurrence
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