Place cells of the rodent hippocampus constitute one of the most striking examples of a correlation between neuronal activity and complex behaviour in mammals. These cells increase their firing rates when the animal traverses specific regions of its surroundings, providing a context-dependent map of the environment. Neuroimaging studies implicate the hippocampus and the parahippocampal region in human navigation. However, these regions also respond selectively to visual stimuli. It thus remains unclear whether rodent place coding has a homologue in humans or whether human navigation is driven by a different, visually based neural mechanism. We directly recorded from 317 neurons in the human medial temporal and frontal lobes while subjects explored and navigated a virtual town. Here we present evidence for a neural code of human spatial navigation based on cells that respond at specific spatial locations and cells that respond to views of landmarks. The former are present primarily in the hippocampus, and the latter in the parahippocampal region. Cells throughout the frontal and temporal lobes responded to the subjects' navigational goals and to conjunctions of place, goal and view.

oscillations in the rat hippocampus have been implicated in sensorimotor integration (Bland, 1986), especially during exploratory and wayfinding behavior. We propose that human cortical activity coordinates sensory information with a motor plan to guide wayfinding behavior to known goal locations. To test this hypothesis, we analyzed invasive recordings from epileptic patients while they performed a spatially immersive, virtual taxi driver task. Consistent with this hypothesis, we found oscillations during both exploratory search and goal-seeking behavior and, in particular, during virtual movement, when sensory information and motor planning were both in flux, compared with periods of self-initiated stillness. oscillations had different topographic and spectral characteristics during searching than during goal-seeking, suggesting that different cortical networks exhibit depending on which cognitive functions are driving behavior (spatial learning during exploration vs orienting to a learned representation during goal-seeking). In contrast, oscillations in the beta band appeared to be related to simple motor planning, likely a variant of the Rolandic mu rhythm. These findings suggest that human cortical oscillations act to coordinate sensory and motor brain activity in various brain regions to facilitate exploratory learning and navigational planning.

Retrieval-induced forgetting (RIF) refers to the finding that retrieving a memory can impair subsequent recall of related memories. Here, the authors present a new model of how the brain gives rise to RIF in both semantic and episodic memory. The core of the model is a recently developed neural network learning algorithm that leverages regular oscillations in feedback inhibition to strengthen weak parts of target memories and to weaken competing memories. The authors use the model to address several puzzling findings relating to RIF, including why retrieval practice leads to more forgetting than simply presenting the target item, how RIF is affected by the strength of competing memories and the strength of the target (to-be-retrieved) memory, and why RIF sometimes generalizes to independent cues and sometimes does not. For all of these questions, the authors show that the model can account for existing results, and they generate novel predictions regarding boundary conditions on these results.

We present a new learning algorithm that leverages oscillations in the strength of neural inhibition to train neural networks. Raising inhibition can be used to identify weak parts of target memories, which are then strengthened. Conversely, lowering inhibition can be used to identify competitors, which are then weakened. To update weights, we apply the Contrastive Hebbian Learning equation to successive time steps of the network. The sign of the weight change equation varies as a function of the phase of the inhibitory oscillation. We show that the learning algorithm can memorize large numbers of correlated input patterns without collapsing and that it shows good generalization to test patterns that do not exactly match studied patterns.

Oscillatory interference models propose a mechanism by which the spatial firing pattern of grid cells can arise from the interaction of multiple oscillators that shift in relative phase. These models produce aspects of the physiological data such as the phase precession dynamics observed in grid cells. However, existing oscillatory interference models did not predict the in-field DC shifts in the membrane potential of grid cells that have been observed during intracellular recordings in navigating animals. Here, we demonstrate that DC shifts can be generated in an oscillatory interference model when half-wave rectified oscillatory inputs are summed by a leaky integrate-and-fire neuron with a long membrane decay constant (100 ms). The non-linear mean of the half-wave rectified input signal is reproduced in the grid cell's membrane potential trace producing the DC shift within field. For shorter values of the decay constant integration is more effective if the input signal, comprising input from 6 head direction selective populations, is temporally spread during in-field epochs; this requires that the head direction selective populations act as velocity controlled oscillators with baseline oscillations that are phase offset from one another. The resulting simulated membrane potential matches several properties of the empirical intracellular recordings, including: in-field DC-shifts, theta-band oscillations, phase precession of both membrane potential oscillations and grid cell spiking activity relative to network theta and a stronger correlation between DC-shift amplitude and firing-rate than between theta-band oscillation amplitude and firing-rate. This work serves to demonstrate that oscillatory interference models can account for the DC shifts in the membrane potential observed during intracellular recordings of grid cells without the need to appeal to attractor dynamics.