Navigating Through Time: A Spatial Navigation Perspective on How the Brain May Encode Time.

Interval timing, which operates on timescales of seconds to minutes, is distributed across multiple brain regions and may use distinct circuit mechanisms as compared to millisecond timing and circadian rhythms. However, its study has proven difficult, as timing on this scale is deeply entangled with other behaviors. Several circuit and cellular mechanisms could generate sequential or ramping activity patterns that carry timing information. Here we propose that a productive approach is to draw parallels between interval timing and spatial navigation, where direct analogies can be made between the variables of interest and the mathematical operations necessitated. Along with designing experiments that isolate or disambiguate timing behavior from other variables, new techniques will facilitate studies that directly address the neural mechanisms that are responsible for interval timing.

Spatial cognition in a virtual reality home-cage extension for freely moving rodents.

Virtual reality (VR) environments are a powerful tool to investigate brain mechanisms involved in the behavior of animals. With this technique, animals are usually head fixed or secured in a harness, and training for cognitively more complex VR paradigms is time consuming. A VR apparatus allowing free animal movement and the constant operator-independent training of tasks would enable many new applications. Key prospective usages include brain imaging of animal behavior when carrying a miniaturized mobile device such as a fluorescence microscope or an optetrode. Here, we introduce the Servoball, a spherical VR treadmill based on the closed-loop tracking of a freely moving animal and feedback counterrotation of the ball. Furthermore, we present the complete integration of this experimental system with the animals' group home cage, from which single individuals can voluntarily enter through a tunnel with radio-frequency identification (RFID)-automated access control and commence experiments. This automated animal sorter functions as a mechanical replacement of the experimenter. We automatically trained rats using visual or acoustic cues to solve spatial cognitive tasks and recorded spatially modulated entorhinal cells. When electrophysiological extracellular recordings from awake behaving rats were performed, head fixation can dramatically alter results, so that any complex behavior that requires head movement is impossible to achieve. We circumvented this problem with the use of the Servoball in open-field scenarios, as it allows the combination of open-field behavior with the recording of nerve cells, along with all the flexibility that a virtual environment brings. This integrated home cage with a VR arena experimental system permits highly efficient experimentation for complex cognitive experiments.NEW & NOTEWORTHY Virtual reality (VR) environments are a powerful tool for the investigation of brain mechanisms. We introduce the Servoball, a VR treadmill for freely moving rodents. The Servoball is integrated with the animals' group home cage. Single individuals voluntarily enter using automated access control. Training is highly time-efficient, even for cognitively complex VR paradigms.

Grid cells correlation structure suggests organized feedforward projections into superficial layers of the medial entorhinal cortex.

Navigation requires integration of external and internal inputs to form a representation of location. Part of this integration is considered to be carried out by the grid cells network in the medial entorhinal cortex (MEC). However, the structure of this neural network is unknown. To shed light on this structure, we measured noise correlations between 508 pairs of simultaneous previously recorded grid cells. We differentiated between pure grid and conjunctive cells (pure grid in Layers II, III, and VI vs. conjunctive in Layers III and V--only Layer III was bi-modal), and devised a new method to classify cell pairs as belonging/not-belonging to the same module. We found that pairs from the same module show significantly more correlations than pairs from different modules. The correlations between pure grid cells decreased in strength as their relative spatial phase increased. However, correlations were mostly at 0 time-lag, suggesting that the source of correlations was not only synaptic, but rather resulted mostly from common input. Given our measured correlations, the two functional groups of grid cells (pure vs. conjunctive), and the known disorganized recurrent connections within Layer II, we propose the following model: conjunctive cells in deep layers form an attractor network whose activity is governed by velocity-controlled signals. A second manifold in Layer II receives organized feedforward projections from the deep layers, giving rise to pure grid cells. Numerical simulations indicate that organized projections induce such correlations as we measure in superficial layers. Our results provide new evidence for the functional anatomy of the entorhinal circuit-suggesting that strong phase-organized feedforward projections support grid fields in the superficial layers.

During hippocampal inactivation, grid cells maintain synchrony, even when the grid pattern is lost.

The grid cell network in the medial entorhinal cortex (MEC) has been subject to thorough testing and analysis, and many theories for their formation have been suggested. To test some of these theories, we re-analyzed data from Bonnevie et al., 2013, in which the hippocampus was inactivated and grid cells were recorded in the rat MEC. We investigated whether the firing associations of grid cells depend on hippocampal inputs. Specifically, we examined temporal and spatial correlations in the firing times of simultaneously recorded grid cells before and during hippocampal inactivation. Our analysis revealed evidence of network coherence in grid cells even in the absence of hippocampal input to the MEC, both in regular grid cells and in those that became head-direction cells after hippocampal inactivation. This favors models, which suggest that phase relations between grid cells in the MEC are dependent on intrinsic connectivity within the MEC.

Dissociation between Postrhinal Cortex and Downstream Parahippocampal Regions in the Representation of Egocentric Boundaries.

Navigation requires the integration of many sensory inputs to form a multi-modal cognitive map of the environment, which is believed to be implemented in the hippocampal region by spatially tuned cells [1-10]. These cells encode various aspects of the environment in a world-based (allocentric) reference frame. Although the cognitive map is represented in allocentric coordinates, the environment is sensed through diverse sensory organs, mostly situated in the animal's head, and therefore represented in sensory and parietal cortices in head-centered egocentric coordinates. Yet it is not clear how and where the brain transforms these head-centered egocentric representations to map-like allocentric representations computed in the hippocampal region. Theoretical modeling has predicted a role for both egocentric and head direction (HD) information in performing an egocentric-allocentric transformation [11-15]. Here, we recorded new data and also used data from a previous study [16]. Adapting a generalized linear model (GLM) classification [17]; we show that the postrhinal cortex (POR) contains a population of pure egocentric boundary cells (EBCs), in contrast with the conjunctive EBCs × HD cells, which we found downstream mostly in the parasubiculum (PaS) and in the medial entorhinal cortex (MEC). Our finding corroborates the idea of a brain network performing an egocentric to allocentric transformation by HD cells. This is a fundamental building block in the formation of the brain's internal cognitive map.