Head direction maps remain stable despite grid map fragmentation

Areas encoding space in the brain contain both representations of position (place cells and grid cells) and representations of azimuth (head direction cells). Previous studies have already suggested that although grid cells and head direction cells reside in the same brain areas, the calculation of head direction is not dependent on the calculation of position. Here we demonstrate that realignment of grid cells does not affect head direction tuning. We analyzed head direction cell data collected while rats performed a foraging task in a multi-compartment environment (the hairpin maze) vs. an open-field environment, demonstrating that the tuning of head direction cells did not change when the environment was divided into multiple sub-compartments, in the hairpin maze. On the other hand, as we have shown previously (Derdikman et al., 2009), the hexagonal firing pattern expressed by grid cells in the open-field broke down into repeating patterns in similar alleys when rats traversed the multi-compartment hairpin maze. The grid-like firing of conjunctive cells, which express both grid properties and head direction properties in the open-field, showed a selective fragmentation of grid-like firing properties in the hairpin maze, while the head directionality property of the same cells remained unaltered. These findings demonstrate that head direction is not affected during the restructuring of grid cell firing fields as a rat actively moves between compartments, thus strengthening the claim that the head direction system is upstream from or parallel to the grid-place system

Parallel Thalamic Pathways for Whisking and Touch Signals in the Rat

In active sensation, sensory information is acquired via movements of sensory organs; rats move their whiskers repetitively to scan the environment, thus detecting, localizing, and identifying objects. Sensory information, in turn, affects future motor movements. How this motor-sensory-motor functional loop is implemented across anatomical loops of the whisker system is not yet known. While inducing artificial whisking in anesthetized rats, we recorded the activity of individual neurons from three thalamic nuclei of the whisker system, each belonging to a different major afferent pathway: paralemniscal, extralemniscal (a recently discovered pathway), or lemniscal. We found that different sensory signals related to active touch are conveyed separately via the thalamus by these three parallel afferent pathways. The paralemniscal pathway conveys sensor motion (whisking) signals, the extralemniscal conveys contact (touch) signals, and the lemniscal pathway conveys combined whisking–touch signals. This functional segregation of anatomical pathways raises the possibility that different sensory-motor processes, such as those related to motion control, object localization, and object identification, are implemented along different motor-sensory-motor loops

Representational drift as a result of implicit regularization

Recent studies show that, even in constant environments, the tuning of single neurons changes over time in a variety of brain regions. This representational drift has been suggested to be a consequence of continuous learning under noise, but its properties are still not fully understood. To investigate the underlying mechanism, we trained an artificial network on a simplified navigational task. The network quickly reached a state of high performance, and many units exhibited spatial tuning. We then continued training the network and noticed that the activity became sparser with time. Initial learning was orders of magnitude faster than ensuing sparsification. This sparsification is consistent with recent results in machine learning, in which networks slowly move within their solution space until they reach a flat area of the loss function. We analyzed four datasets from different labs, all demonstrating that CA1 neurons become sparser and more spatially informative with exposure to the same environment. We conclude that learning is divided into three overlapping phases: (i) Fast familiarity with the environment; (ii) slow implicit regularization; and (iii) a steady state of null drift. The variability in drift dynamics opens the possibility of inferring learning algorithms from observations of drift statistics

Proposed Scheme of Afferent Conduction of Active-Touch Signals

<p>Whisking signals (W) are conveyed by the paralemniscal pathway (para) via the POm and are proposed to involve whisking control. Touch signals (T) are conveyed by the extralemniscal pathway (extra) via VPMvl, and are proposed to involve processing of object location (“where”). Combined whisking–touch signals (WT) are conveyed by the lemniscal pathway via VPMdm, and are proposed to involve processing of object identity (“what”).</p