Movement dependence and layer specificity of entorhinal phase precession in two-dimensional environments

Phase coding of spatial representations in the human entorhinal cortex

The grid-like activity pattern of cells in the mammalian entorhinal cortex provides an internal reference frame for allocentric self-localization. The same neurons maintain robust phase couplings with local field oscillations. We found that neurons of the human entorhinal cortex display consistent spatial and temporal phase locking between spikes and slow gamma band local field potentials (LFPs) during virtual navigation. The phase locking maintained an environment-specific map over time. The phase tuning of spikes to the slow gamma band LFP revealed spatially periodic phase grids with environment-dependent scaling and consistent alignment with the environment. Using a Bayesian decoding model, we could predict the avatar's position with near perfect accuracy and, to a lesser extent, that of heading direction as well. These results imply that the phase of spikes relative to spatially modulated gamma oscillations encode allocentric spatial positions. We posit that a joint spatiotemporal phase code can implement the combined neural representation of space and time in the human entorhinal cortex.

Burst activity plays no role in the field-to-field variability and rate remapping of grid cells

Grid cells in rodent medial entorhinal cortex are thought to play a key role for spatial navigation. When the animal is freely moving in an open arena the firing fields of each grid cell tend to form a highly regular, hexagonal lattice spanning the environment. However, firing rates vary from field to field and change under contextual modifications, whereas the field locations shift at most by a small amount under such "rate remapping." The observed differences in firing rate could reflect overall activity changes or changes in the detailed spike-train statistics. As these two alternatives imply distinct neural coding schemes, we investigated whether temporal firing patterns vary from field to field and whether they change under rate remapping. Focusing on short time scales, we found that the proportion of bursts compared to all discharge events is similar in all firing fields of a given grid cell and does not change under rate remapping. For each cell, mean firing rates with bursts are proportional to mean firing rates without bursts. However, this ratio varies across cells. Additionally, we looked at how rate remapping relates to entorhinal theta-frequency oscillations. Theta-phase coding was preserved despite firing-rate changes from rate remapping but we did not observe differences between the first and second half of the theta cycle, as had been reported for CA1. Our results indicate that both, the heterogeneity between firing fields and rate remapping, are not due to altered firing patterns on short time scales but reflect location-specific changes at the firing-rate level.

Phase precession in the human hippocampus and entorhinal cortex

Knowing where we are, where we have been, and where we are going is critical to many behaviors, including navigation and memory. One potential neuronal mechanism underlying this ability is phase precession, in which spatially tuned neurons represent sequences of positions by activating at progressively earlier phases of local network theta oscillations. Based on studies in rodents, researchers have hypothesized that phase precession may be a general neural pattern for representing sequential events for learning and memory. By recording human single-neuron activity during spatial navigation, we show that spatially tuned neurons in the human hippocampus and entorhinal cortex exhibit phase precession. Furthermore, beyond the neural representation of locations, we show evidence for phase precession related to specific goal states. Our findings thus extend theta phase precession to humans and suggest that this phenomenon has a broad functional role for the neural representation of both spatial and non-spatial information.

Optogenetic pacing of medial septum parvalbumin-positive cells disrupts temporal but not spatial firing in grid cells

Grid cells in the medial entorhinal cortex (MEC) exhibit remarkable spatial activity patterns with spikes coordinated by theta oscillations driven by the medial septal area (MSA). Spikes from grid cells progress relative to the theta phase in a phenomenon called phase precession, which is suggested as essential to create the spatial periodicity of grid cells. Here, we show that optogenetic activation of parvalbumin-positive (PV⁺) cells in the MSA enabled selective pacing of local field potential (LFP) oscillations in MEC. During optogenetic stimulation, the grid cells were locked to the imposed pacing frequency but kept their spatial patterns. Phase precession was abolished, and speed information was no longer reflected in the LFP oscillations but was still carried by rate coding of individual MEC neurons. Together, these results support that theta oscillations are not critical to the spatial pattern of grid cells and do not carry a crucial velocity signal.

Replay as wavefronts and theta sequences as bump oscillations in a grid cell attractor network

Grid cells fire in sequences that represent rapid trajectories in space. During locomotion, theta sequences encode sweeps in position starting slightly behind the animal and ending ahead of it. During quiescence and slow wave sleep, bouts of synchronized activity represent long trajectories called replays, which are well-established in place cells and have been recently reported in grid cells. Theta sequences and replay are hypothesized to facilitate many cognitive functions, but their underlying mechanisms are unknown. One mechanism proposed for grid cell formation is the continuous attractor network. We demonstrate that this established architecture naturally produces theta sequences and replay as distinct consequences of modulating external input. Driving inhibitory interneurons at the theta frequency causes attractor bumps to oscillate in speed and size, which gives rise to theta sequences and phase precession, respectively. Decreasing input drive to all neurons produces traveling wavefronts of activity that are decoded as replays.

Hippocampal Sequences During Exploration: Mechanisms and Functions

Although the hippocampus plays a critical role in spatial and episodic memories, the mechanisms underlying memory formation, stabilization, and recall for adaptive behavior remain relatively unknown. During exploration, within single cycles of the ongoing theta rhythm that dominates hippocampal local field potentials, place cells form precisely ordered sequences of activity. These neural sequences result from the integration of both external inputs conveying sensory-motor information, and intrinsic network dynamics possibly related to memory processes. Their endogenous replay during subsequent sleep is critical for memory consolidation. The present review discusses possible mechanisms and functions of hippocampal theta sequences during exploration. We present several lines of evidence suggesting that these neural sequences play a key role in information processing and support the formation of initial memory traces, and discuss potential functional distinctions between neural sequences emerging during theta vs. awake sharp-wave ripples.

A single-cell spiking model for the origin of grid-cell patterns

Spatial cognition in mammals is thought to rely on the activity of grid cells in the entorhinal cortex, yet the fundamental principles underlying the origin of grid-cell firing are still debated. Grid-like patterns could emerge via Hebbian learning and neuronal adaptation, but current computational models remained too abstract to allow direct confrontation with experimental data. Here, we propose a single-cell spiking model that generates grid firing fields via spike-rate adaptation and spike-timing dependent plasticity. Through rigorous mathematical analysis applicable in the linear limit, we quantitatively predict the requirements for grid-pattern formation, and we establish a direct link to classical pattern-forming systems of the Turing type. Our study lays the groundwork for biophysically-realistic models of grid-cell activity.

Phase precession: a neural code underlying episodic memory?

In the hippocampal formation, the sequential activation of place-specific cells represents a conceptual model for the spatio-temporal events that assemble episodic memories. The imprinting of behavioral sequences in hippocampal networks might be achieved via spike-timing-dependent plasticity and phase precession of the spiking activity of neurons. It is unclear, however, whether phase precession plays an active role by enabling sequence learning via synaptic plasticity or whether phase precession passively reflects retrieval dynamics. Here we examine these possibilities in the context of potential mechanisms generating phase precession. Knowledge of these mechanisms would allow to selectively alter phase precession and test its role in episodic memory. We finally review the few successful approaches to degrade phase precession and the resulting impact on behavior.

Cell Type-Specific Differences in Spike Timing and Spike Shape in the Rat Parasubiculum and Superficial Medial Entorhinal Cortex

The medial entorhinal cortex (MEC) and the adjacent parasubiculum are known for their elaborate spatial discharges (grid cells, border cells, etc.) and the precessing of spikes relative to the local field potential. We know little, however, about how spatio-temporal firing patterns map onto cell types. We find that cell type is a major determinant of spatio-temporal discharge properties. Parasubicular neurons and MEC layer 2 (L2) pyramids have shorter spikes, discharge spikes in bursts, and are theta-modulated (rhythmic, locking, skipping), but spikes phase-precess only weakly. MEC L2 stellates and layer 3 (L3) neurons have longer spikes, do not discharge in bursts, and are weakly theta-modulated (non-rhythmic, weakly locking, rarely skipping), but spikes steeply phase-precess. The similarities between MEC L3 neurons and MEC L2 stellates on one hand and parasubicular neurons and MEC L2 pyramids on the other hand suggest two distinct streams of temporal coding in the parahippocampal cortex.

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We find cell type-specific differences in spike shape, burstiness, and phase precession

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In vivo cell type specificity does not match predictions from previous in vitro studies

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Anatomical identity is a major determinant of spike patterns in the parahippocampal cortex

Cell-Type Specific Phase Precession in Layer II of the Medial Entorhinal Cortex

The identity of phase-precessing cells in the entorhinal cortex is unknown. Here, we used a classifier derived from cell-attached recordings to separate putative pyramidal cells and putative stellate cells recorded extracellularly in layer II of the medial entorhinal cortex in rats. Using a novel method to identify single runs as temporal periods of elevated spiking activity, we find that both cell types show phase precession but putative stellate cells show steeper slopes of phase precession and larger phase ranges. As the two classes of cells have different projection patterns, phase precession is differentially passed on to different subregions of the hippocampal formation.

SIGNIFICANCE STATEMENT

It is a great challenge for neuroscience to reveal the cellular basis of cognitive functions. One such function is the ability to learn and recollect temporal sequences of events. The representation of sequences in the brain is thought to require temporally structured activity of nerve cells. How different types of neurons generate temporally structured activity is currently unknown. In the present study, we use a computational classification procedure to separate different cell types and find that a subpopulation of cells, so-called stellate neurons, exhibits clear temporal coding. Contrary to the stellate cells, pyramidal cells show weaker temporal coding. This discovery sheds light on the cellular basis of temporal coding in the brain.

State-dependencies of learning across brain scales

Learning is a complex brain function operating on different time scales, from milliseconds to years, which induces enduring changes in brain dynamics. The brain also undergoes continuous “spontaneous” shifts in states, which, amongst others, are characterized by rhythmic activity of various frequencies. Besides the most obvious distinct modes of waking and sleep, wake-associated brain states comprise modulations of vigilance and attention. Recent findings show that certain brain states, particularly during sleep, are essential for learning and memory consolidation. Oscillatory activity plays a crucial role on several spatial scales, for example in plasticity at a synaptic level or in communication across brain areas. However, the underlying mechanisms and computational rules linking brain states and rhythms to learning, though relevant for our understanding of brain function and therapeutic approaches in brain disease, have not yet been elucidated. Here we review known mechanisms of how brain states mediate and modulate learning by their characteristic rhythmic signatures. To understand the critical interplay between brain states, brain rhythms, and learning processes, a wide range of experimental and theoretical work in animal models and human subjects from the single synapse to the large-scale cortical level needs to be integrated. By discussing results from experiments and theoretical approaches, we illuminate new avenues for utilizing neuronal learning mechanisms in developing tools and therapies, e.g., for stroke patients and to devise memory enhancement strategies for the elderly.