Few-shot learning of predictive features with dendrites and behavioural timescale synaptic plasticity in the hippocampus

Abstract

When an animal enters a new environment, neurons in the hippocampus begin to map out the space. They become selectively responsive to features like the animals location, the position of rewards, and the presence of stimuli relevant to navigating or performing a specific task. With experience, hippocampal neurons also develop behaviour-related biases. It is thus believed that the hippocampus encodes multi-sensory, behaviourally-relevant cognitive maps of environments that are critical to navigation, learning and guiding behaviour. The predictive learning hypothesis proposes that these complex maps emerge because a core goal of the brain is to learn to predict the features of its environment. In sensory cortex, predictive learning provides a compelling explanation of anticipatory and error-like sensory responses. Pyramidal neurons receive top-down and bottom-up inputs to their proximal basal and distal apical dendrites, respectively. These complementary inputs streams are thought to enable them to act as comparison units, signaling discrepancies between predictive and sensory inputs. In the hippocampus, however, the potential link between pyramidal neurons and predictive learning is still underexplored. Here, we investigate the possibility that pyramidal neurons perform a similar comparator function in the hippocampus. In our model, two-compartment pyramidal neurons receive sensory information about salient features of the environment at their distal apical dendrites which is compared to tuned spatial inputs received more proximally to their cell body. We demonstrate how predictive learning implemented in this circuit using behavioural timescale synaptic plasticity and distal apical inhibition can explain a variety of spatial and behaviourally-relevant features encoded in the hippocampus. We also lay out key predictions for validating our model experimentally. As such, our work helps bridge an important gap in the literature on predictive learning in the hippocampus and set the stage for more robust experimental validation of this prominent hypothesis.