Topic: Gaussian process regression

The brain forms cognitive maps of relational knowledge, an organizing principle thought to underlie our ability to generalize and make inferences. Such map-like representations may facilitate goal-directed behavior by enabling generalization of information across related states. However, it is not fully understood what neural and computational mechanisms give rise to map-based generalization, nor how a relevant map may be selected when a stimulus is embedded in multiple relational structures. Here, we find that both spatial and temporal cognitive maps influence reward generalization in a choic

Modification of synaptic connections is thought to be the core mechanism of long-term memory formation. Despite the recent experimental development, there are still challenges inferring the synaptic plasticity rule from the experimental data. Here, we considered a biologically plausible synaptic plasticity model, and explored its key properties that enable efficient inference. As a recent theoretical work suggested the firing rates to be the most critical under in vivo-like conditions, we considered firing-rate dependent plasticity and its matrix representation. To enable efficient recovery