When learning new environments, rats often pause at decision points and look back and forth over their possible trajectories as if they were imagining the future outcome of their actions, a behavior termed “Vicarious trial and error” (VTE). As the animal learns the environmental configuration, rats change from deliberative to habitual behavior, and VTE tends to disappear, suggesting a functional relevance in the early stages of learning. Despite the extensive research on spatial navigation, learning and VTE in the rat model, fewer studies have focused on humans. Here, we tested whether head-scanning behaviors that humans typically exhibit during spatial navigation are as predictive of spatial learning as in the rat. Subjects performed a goal-oriented virtual navigation task in a symmetric environment. Spatial learning was assessed through the analysis of trajectories, timings, and head orientations, under habitual and deliberative spatial navigation conditions. As expected, we found that trajectory length and duration decreased with the trial number, implying that subjects learned the spatial configuration of the environment over trials. Interestingly, IdPhi (a standard metric of VTE) also decreased with the trial number, suggesting that humans benefit from the same head-orientation scanning behavior as rats at spatial decision-points. Moreover, IdPhi captured exclusively at the first decision-point of each trial, was correlated with trial trajectory duration and length. Our findings demonstrate that in VTE is a signature of the stage of spatial learning in humans, and can be used to predict performance in navigation tasks with high accuracy.

Many hippocampal cell types are characterized by a progressive increase in scale along the dorsal-to-ventral axis, such as in the cases of head-direction, grid and place cells. Also located in the medial entorhinal cortex (MEC), border cells would be expected to benefit from such scale modulations. However, this phenomenon has not been experimentally observed. Grid cells in the MEC of mammals integrate velocity related signals to map the environment with characteristic hexagonal tessellation patterns. Due to the noisy nature of these input signals, path integration processes tend to accumulate errors as animals explore the environment, leading to a loss of grid-like activity. It has been suggested that border-to-grid cells' associations minimize the accumulated grid cells' error when rodents explore enclosures. Thus, the border-grid interaction for error minimization is a suitable scenario to study the effects of border cell scaling within the context of spatial representation. In this study, we computationally address the question of (i) border cells' scale from the perspective of their role in maintaining the regularity of grid cells' firing fields, as well as (ii) what are the underlying mechanisms of grid-border associations relative to the scales of both grid and border cells. Our results suggest that for optimal contribution to grid cells' error minimization, border cells should express smaller firing fields relative to those of the associated grid cells, which is consistent with the hypothesis of border cells functioning as spatial anchoring signals.

The hippocampal formation displays a wide range of physiological responses to different spatial manipulations of the environment. However, very few attempts have been made to identify core computational principles underlying those hippocampal responses. Here, we capitalize on the observation that the entorhinal-hippocampal complex (EHC) forms a closed loop and projects inhibitory signals “countercurrent” to the trisynaptic pathway to build a self-supervised model that learns to reconstruct its own inputs by error backpropagation. The EHC is then abstracted as an autoencoder, with the hidden layers acting as an information bottleneck. With the inputs mimicking the firing activity of lateral and medial entorhinal cells, our model is shown to generate place cells and to respond to environmental manipulations as observed in rodent experiments. Altogether, we propose that the hippocampus builds conjunctive compressed representations of the environment by learning to reconstruct its own entorhinal inputs via gradient descent.

The principles governing the formation of episodic memories from the continuous stream of sensory stimuli are not fully understood. Theoretical models of the hippocampus propose that the representational format of episodic memories comprise oscillations in the theta frequency band (2-8 Hz) that set the time boundaries in which discrete events are bound encoded in the gamma frequency range (>30 Hz). We investigated this temporal segmentation and binding process by analyzing the intracranial EEG (iEEG) of surgically implanted epileptic patients performing a virtual-navigation task. We found a positive correlation between sensory information density encountered by the subject and hippocampal theta-frequency, suggesting that the human hippocampus normalizes the information content of episodic memories relative to the density of sensory information. This interpretation is further supported by the observation that as a marker of mnemonic encoding, i.e. the amount of persistent gamma events, directly correlates with sensory information density, gamma-frequency power and the phase relation between theta and gamma oscillations remain constant. Using a theoretical model of the hippocampus, we build a model that analogously displays a similar normalization of gamma episodes per theta cycle relative to information density by accounting for the physiological signatures of theta-gamma coding through combining fast and slow inhibitory feedback. We propose that this intrinsic normalization mechanism optimizes the trade-off between the discretization and compression of continuous experience relative to the limited capacity of episodic memory. Summary We move in continuous time and space, yet we can encode and recall discrete episodes from our past experiences. The neural mechanism behind this discretization is not fully understood. It has been previously observed that rodent locomotion modulates ongoing hippocampal theta rhythms. Thus, raising the question of whether these slow rhythms bind events together during a single oscillatory cycle relative to the movement speed or overall information density. We quantified the effects of increasing locomotion and sensory information in modulating theta oscillations during virtual navigation with intracranial hippocampal activity from human epileptic patients.. We observed hippocampal theta waves increased with higher speed and higher sensory demands, thereby maintaining constant information per oscillatory cycle. These results highlight the role of hippocampal theta oscillations in discretizing ongoing experience relative to the available information and explain how episodic memory integrates a fixed number of items per oscillatory theta cycle irrespective of richness of the external world.