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Review
, 16 (2), 130-8

Memory, Navigation and Theta Rhythm in the Hippocampal-Entorhinal System

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Review

Memory, Navigation and Theta Rhythm in the Hippocampal-Entorhinal System

György Buzsáki et al. Nat Neurosci.

Abstract

Theories on the functions of the hippocampal system are based largely on two fundamental discoveries: the amnestic consequences of removing the hippocampus and associated structures in the famous patient H.M. and the observation that spiking activity of hippocampal neurons is associated with the spatial position of the rat. In the footsteps of these discoveries, many attempts were made to reconcile these seemingly disparate functions. Here we propose that mechanisms of memory and planning have evolved from mechanisms of navigation in the physical world and hypothesize that the neuronal algorithms underlying navigation in real and mental space are fundamentally the same. We review experimental data in support of this hypothesis and discuss how specific firing patterns and oscillatory dynamics in the entorhinal cortex and hippocampus can support both navigation and memory.

Figures

Figure 1
Figure 1
Relationship between navigation and memory. (a) Path integration (also known as dead reckoning) is based on self-referenced information by keeping track of travel distances (time elapsed multiplied by speed) and direction of turns. Calculating translocation relative to the start location allows the animal to return to the start along the shortest (homing) path. (b) Map-based navigation is supported by the relationships among visible or otherwise detectable landmarks. A map is constructed by exploration (path integration). (c) Episodic memory is ‘mental travel’ in time and space referenced to self. (d) Semantic memory is explicit representation of living things, objects, places and events without temporal or contextual references. Semantic knowledge can be acquired through multiple episodes with common elements. We hypothesize that the evolutionary roots of episodic and semantic memory systems are the dead reckoning and landmark-based forms of navigation, respectively.
Figure 2
Figure 2
Grid cells and place cells. (a–d) Firing patterns of entorhinal (a,c) and hippocampal (b,d) principal cells in a two-dimensional open field (a,b) and one-dimensional track (c,d). (a) Grid cell from layer 2 of the entorhinal cortex recorded from a rat exploring a 2-m cylinder. Note increased firing rates (warmer colors) throughout the environment at the apexes of tiling triangles. (b) Singular place field of a CA1 pyramidal neuron recorded from a rodent exploring a square 1 × 1 m. Firing rate of the neuron is color-coded. (c) On a 2-m-long linear track, entorhinal neurons fire at regular intervals (red arrows) but at different positions during left and right journeys. (d) Firing rates of pyramidal cells on a track. CA1 pyramidal cells have typically a single field, present mainly in one direction of travel. Each row represents a neuron. Firing rates are peak-normalized, color-coded and ordered by their peak rates during right or left direction (arrow) of travel. Panels a,c reproduced with permission from ref. , b from ref. and d from ref. .
Figure 3
Figure 3
Modular organization of the grid-cell network. (a) Stepwise increase of grid spacing at successive dorsoventral levels of medial entorhinal cortex. Spatial autocorrelograms for four example cells (one per dorsoventral module). (b) Remapping of hippocampal place cells in two environments. Top panels show responses of three grid cells to a change in the environment. Independent responses are illustrated by different degrees of rotation and translation. Bottom panels show inputs from the grid cells, each from a different module (purple, green and orange), at different locations in the environments. The three grid cells provide input to a particular CA3 cell (white spot at left) sufficient to cause it to fire when, and only when, the nodes of the three grids overlap. This occurs only at one location in this example. In the second environment, the altered coactivity of the grid cells activates a different subset of place cells at each location, and global remapping is observed in hippocampal place ensembles. Panel a reproduced with permission after ref. , b after ref. .
Figure 4
Figure 4
Cell assembly sequences, space and time tracking. (a) During physical travel, successive assemblies of neurons (1 to n) respond sequentially owing to the changing constellation of environmental landmarks and/or proprioceptive information from the body (top). During mental travel, sequential activation is supported by self-organized patterning. Not only first order (neighbor) but also higher order (non-neighbor) connections can be represented in strongly connected recurrent networks. (b) Inferring elapsed time from self-organized cell assembly sequences during wheel running in a spontaneous alternation task. Top left, the rat’s travel path superimposed on the maze. The start area is the wheel (bottom). Top right, normalized firing rate sequence of neurons during wheel running, ordered by the latency of their peak firing rates (each line represents a cell). Bottom, accumulating errors of time prediction (in seconds) calculated from a probabilistic model for inferring elapsed time from the phases of spikes with respect to theta oscillation. Note high precision of time prediction during the entire episode of wheel running. (c) Time tracking by neuron sequences during the delay part of a task of a working memory go/no-go task in a linear maze (top). The delay varied between 10 and 20 s in consecutive blocks, as indicated by the length of the yellow lines (bottom). Some neurons maintain their spike timing (left), whereas others ‘re-time’ (right) when the length is abruptly changed. Panel b reprinted with permission from ref. , c from ref. .
Figure 5
Figure 5
Theta oscillations link assembly sequences. (a) Overlapping place fields of two hippocampal neurons (green and blue) on a track. (b) Theta phase of each spike as a function of position in the place fields of the two neurons. Note precession of spikes from late to early phases as the rat crosses the place fields. Two theta cycles are shown for clarity. (c) Cross-correlation between the reference (blue) and overlapping (green) place cells. Δ time is the time lag between the spikes of two neurons within the theta cycle (‘theta time’). (d) Correlation between the distances of place field peaks and theta-scale time lags for >3,000 pairs of CA1 place cells. Note that the duration of the theta cycle limits distance resolution (red sigmoid curve). Panels a–c reproduced with permission after ref. , d after ref. .
Figure 6
Figure 6
Cell assembly segregation role of theta oscillation. (a) Place fields of two pyramidal cells (P1 and P2) and a putative basket cell interneuron (IN) on a linear track. (b) Mean phase precession of P1 (red stars) and P2 (magenta dots), superimposed on the interneuron’s color-coded and smoothed density of firing in theta phase space. Note the similar phase slopes of P1 and the interneuron and firing minima of the interneuron in the phase space of P2. (c) Model of theta phase selection by means of inhibition between competing assemblies. The interneuron is driven by the assembly of which P1 is a member and prevents discharge activity of a competing assembly of which P2 is a member. (d) Cartoon of phase segregation of seven cell assemblies in the entire phase space of the theta cycle (top). Perturbation of perisomatic inhibition reduces assembly segregation so that ‘information’ from the scrambled assembly sequence will become difficult to read out by downstream observer mechanisms (bottom). Panel a reproduced with permission after ref. .

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