Functional connectivity of the entorhinal–hippocampal space circuit

The mammalian space circuit is known to contain several functionally specialized cell types, such as place cells in the hippocampus and grid cells, head-direction cells and border cells in the medial entorhinal cortex (MEC). The interaction between the entorhinal and hippocampal spatial representations is poorly understood, however. We have developed an optogenetic strategy to identify functionally defined cell types in the MEC that project directly to the hippocampus. By expressing channelrhodopsin-2 (ChR2) selectively in the hippocampus-projecting subset of entorhinal projection neurons, we were able to use light-evoked discharge as an instrument to determine whether specific entorhinal cell groups—such as grid cells, border cells and head-direction cells—have direct hippocampal projections. Photoinduced firing was observed at fixed minimal latencies in all functional cell categories, with grid cells as the most abundant hippocampus-projecting spatial cell type. We discuss how photoexcitation experiments can be used to distinguish the subset of hippocampus-projecting entorhinal neurons from neurons that are activated indirectly through the network. The functional breadth of entorhinal input implied by this analysis opens up the potential for rich dynamic interactions between place cells in the hippocampus and different functional cell types in the entorhinal cortex (EC).     

Coordinated learning of grid cell and place cell spatial and temporal properties: multiple scales,attention and oscillations

A neural model proposes how entorhinal grid cells and hippocampal place cells may develop as spatial categories in a hierarchy of self-organizing maps (SOMs). The model responds to realistic rat navigational trajectories by learning both grid cells with hexagonal grid firing fields of multiple spatial scales, and place cells with one or more firing fields, that match neurophysiological data about their development in juvenile rats. Both grid and place cells can develop by detecting, learning and remembering the most frequent and energetic co-occurrences of their inputs. The model''s parsimonious properties include: similar ring attractor mechanisms process linear and angular path integration inputs that drive map learning; the same SOM mechanisms can learn grid cell and place cell receptive fields; and the learning of the dorsoventral organization of multiple spatial scale modules through medial entorhinal cortex to hippocampus (HC) may use mechanisms homologous to those for temporal learning through lateral entorhinal cortex to HC (‘neural relativity’). The model clarifies how top-down HC-to-entorhinal attentional mechanisms may stabilize map learning, simulates how hippocampal inactivation may disrupt grid cells, and explains data about theta, beta and gamma oscillations. The article also compares the three main types of grid cell models in the light of recent data.     

Independence of landmark and self-motion-guided navigation: a different role for grid cells

Recent interest in the neural bases of spatial navigation stems from the discovery of neuronal populations with strong, specific spatial signals. The regular firing field arrays of medial entorhinal grid cells suggest that they may provide place cells with distance information extracted from the animal''s self-motion, a notion we critically review by citing new contrary evidence. Next, we question the idea that grid cells provide a rigid distance metric. We also discuss evidence that normal navigation is possible using only landmarks, without self-motion signals. We then propose a model that supposes that information flow in the navigational system changes between light and dark conditions. We assume that the true map-like representation is hippocampal and argue that grid cells have a crucial navigational role only in the dark. In this view, their activity in the light is predominantly shaped by landmarks rather than self-motion information, and so follows place cell activity; in the dark, their activity is determined by self-motion cues and controls place cell activity. A corollary is that place cell activity in the light depends on non-grid cells in ventral medial entorhinal cortex. We conclude that analysing navigational system changes between landmark and no-landmark conditions will reveal key functional properties.     

How to build a grid cell

Neurons in the medial entorhinal cortex fire action potentials at regular spatial intervals, creating a striking grid-like pattern of spike rates spanning the whole environment of a navigating animal. This remarkable spatial code may represent a neural map for path integration. Recent advances using patch-clamp recordings from entorhinal cortex neurons in vitro and in vivo have revealed how the microcircuitry in the medial entorhinal cortex may contribute to grid cell firing patterns, and how grid cells may transform synaptic inputs into spike output during firing field crossings. These new findings provide key insights into the ingredients necessary to build a grid cell.     

Inducible and cell-type restricted manipulation in the entorhinal cortex

The entorhinal cortex functions as the gateway to the hippocampal formation. However, its role in formation and consolidation of hippocampus-dependent memory remains relatively unexplored. In this issue of Neuron, Yasuda and Mayford report an elegant cell-type restricted inducible transgenic mouse overexpressing a mutant form of CaM kinase II selectively in superficial layers of medial entorhinal cortex and its upstream regions. These animals display a selective spatial memory deficit during the immediate posttraining period as well as during acquisition in the Morris water maze. Similar to the hippocampus, this time-limited involvement of entorhinal cortex in spatial memory processing suggests a crucial role for hippocampal-entorhinal circuitry in spatial memory formation.     

Voltage dependence of subthreshold resonance frequency in layer II of medial entorhinal cortex

The resonance properties of individual neurons in entorhinal cortex (EC) may contribute to their functional properties in awake, behaving rats. Models propose that entorhinal grid cells could arise from shifts in the intrinsic frequency of neurons caused by changes in membrane potential owing to depolarizing input from neurons coding velocity. To test for potential changes in intrinsic frequency, we measured the resonance properties of neurons at different membrane potentials in neurons in medial and lateral EC. In medial entorhinal neurons, the resonant frequency of individual neurons decreased in a linear manner as the membrane potential was depolarized between -70 and -55 mV. At more hyperpolarized membrane potentials, cells asymptotically approached a maximum resonance frequency. Consistent with the previous studies, near resting potential, the cells of the medial EC possessed a decreasing gradient of resonance frequency along the dorsal to ventral axis, and cells of the lateral EC lacked resonant properties, regardless of membrane potential or position along the medial to lateral axis within lateral EC. Application of 10 μM ZD7288, the H-channel blocker, abolished all resonant properties in MEC cells, and resulted in physiological properties very similar to lateral EC cells. These results on resonant properties show a clear change in frequency response with depolarization that could contribute to the generation of grid cell firing properties in the medial EC.     

How Entorhinal Grid Cells May Learn Multiple Spatial Scales from a Dorsoventral Gradient of Cell Response Rates in a Self-organizing Map

Place cells in the hippocampus of higher mammals are critical for spatial navigation. Recent modeling clarifies how this may be achieved by how grid cells in the medial entorhinal cortex (MEC) input to place cells. Grid cells exhibit hexagonal grid firing patterns across space in multiple spatial scales along the MEC dorsoventral axis. Signals from grid cells of multiple scales combine adaptively to activate place cells that represent much larger spaces than grid cells. But how do grid cells learn to fire at multiple positions that form a hexagonal grid, and with spatial scales that increase along the dorsoventral axis? In vitro recordings of medial entorhinal layer II stellate cells have revealed subthreshold membrane potential oscillations (MPOs) whose temporal periods, and time constants of excitatory postsynaptic potentials (EPSPs), both increase along this axis. Slower (faster) subthreshold MPOs and slower (faster) EPSPs correlate with larger (smaller) grid spacings and field widths. A self-organizing map neural model explains how the anatomical gradient of grid spatial scales can be learned by cells that respond more slowly along the gradient to their inputs from stripe cells of multiple scales, which perform linear velocity path integration. The model cells also exhibit MPO frequencies that covary with their response rates. The gradient in intrinsic rhythmicity is thus not compelling evidence for oscillatory interference as a mechanism of grid cell firing. A response rate gradient combined with input stripe cells that have normalized receptive fields can reproduce all known spatial and temporal properties of grid cells along the MEC dorsoventral axis. This spatial gradient mechanism is homologous to a gradient mechanism for temporal learning in the lateral entorhinal cortex and its hippocampal projections. Spatial and temporal representations may hereby arise from homologous mechanisms, thereby embodying a mechanistic “neural relativity” that may clarify how episodic memories are learned.     

CaMKII activation in the entorhinal cortex disrupts previously encoded spatial memory

To investigate the role of the entorhinal cortex in memory at a molecular level, we developed transgenic mice in which transgene expression was inducible and limited to the superficial layers of the medial entorhinal cortex, pre- and parasubiculum. We found that expression of a constitutively active mutant form of CaMKII in these structures disrupted spatial memory formation. Immediate post-training activation of the transgene disrupted previously established memory while transgene activation 3 weeks following the training was ineffective. These results demonstrate that, similar to the hippocampus, the entorhinal cortex plays a time-limited role in spatial memory formation but is not a final cortical repository of long-term memory. Moreover, these results suggest that the indiscriminate activation of CaMKII is able to disrupt preexisting memories, possibly by altering the pattern of synaptic weight changes that are thought to form the basis of the memory trace.     

Impaired spatial representation in CA1 after lesion of direct input from entorhinal cortex

Place-specific firing in the hippocampus is determined by path integration-based spatial representations in the grid-cell network of the medial entorhinal cortex. Output from this network is conveyed directly to CA1 of the hippocampus by projections from principal neurons in layer III, but also indirectly by axons from layer II to the dentate gyrus and CA3. The direct pathway is sufficient for spatial firing in CA1, but it is not known whether similar firing can also be supported by the input from CA3. To test this possibility, we made selective lesions in layer III of medial entorhinal cortex by local infusion of the neurotoxin gamma-acetylenic GABA. Firing fields in CA1 became larger and more dispersed after cell loss in layer III, whereas CA3 cells, which receive layer II input, still had sharp firing fields. Thus, the direct projection is necessary for precise spatial firing in the CA1 place cell population.     

Path integration and the neural basis of the 'cognitive map'

The hippocampal formation can encode relative spatial location, without reference to external cues, by the integration of linear and angular self-motion (path integration). Theoretical studies, in conjunction with recent empirical discoveries, suggest that the medial entorhinal cortex (MEC) might perform some of the essential underlying computations by means of a unique, periodic synaptic matrix that could be self-organized in early development through a simple, symmetry-breaking operation. The scale at which space is represented increases systematically along the dorsoventral axis in both the hippocampus and the MEC, apparently because of systematic variation in the gain of a movement-speed signal. Convergence of spatially periodic input at multiple scales, from so-called grid cells in the entorhinal cortex, might result in non-periodic spatial firing patterns (place fields) in the hippocampus.     

Consolidation of passive avoidance learning is associated with transient increases of polysialylated neurons in layer II of the rat medial temporal cortex

Within the rat medial temporal lobe, transient modulations of neural cell adhesion molecule (NCAM) polysialylation have been observed to follow spatial learning. These have been attributed to neuroplastic events associated with the processing of information destined for long term memory consolidation. To determine if similar events are associated with avoidance learning, we investigated change in polysialylated cell number in the entorhinal, perirhinal, and piriform cortex, following acquisition of a passive avoidance task in the rat. Direct quantification of polysialylated neurons in layer II of these cortical regions revealed a significant increase in polysialylated cell frequency at 12 h following passive avoidance training. Unlike spatial learning, the increased expression of polysialylated neurons persisted for up to 24-48 h following training. In the more dorsal aspect of the perirhinal/entorhinal cortex, this increase was found to be specific to learning, as it was not observed in animals rendered amnesic with scopolamine. By contrast, change in polysialylated cell frequency in the ventral aspect of the medial temporal lobe was only partially reduced by amnesic doses of scopolamine. The persisting activation of NCAM polysialylation in the more dorsal aspects of the perirhinal and entorhinal cortex is suggested to reflect the need for more extensive synaptic alterations, as compared to those required for the consolidation of spatial learning. Moreover, the neuroplastic modulations observed in the more ventral regions of the entorhinal and perirhinal cortex appear to be a unique aspect of avoidance conditioning that reflects the activation of alternative learning strategies associated with motivational and/or contextual parameters of the task.     

Functional correlates of the lateral and medial entorhinal cortex: objects,path integration and local–global reference frames

The hippocampus receives its major cortical input from the medial entorhinal cortex (MEC) and the lateral entorhinal cortex (LEC). It is commonly believed that the MEC provides spatial input to the hippocampus, whereas the LEC provides non-spatial input. We review new data which suggest that this simple dichotomy between ‘where’ versus ‘what’ needs revision. We propose a refinement of this model, which is more complex than the simple spatial–non-spatial dichotomy. MEC is proposed to be involved in path integration computations based on a global frame of reference, primarily using internally generated, self-motion cues and external input about environmental boundaries and scenes; it provides the hippocampus with a coordinate system that underlies the spatial context of an experience. LEC is proposed to process information about individual items and locations based on a local frame of reference, primarily using external sensory input; it provides the hippocampus with information about the content of an experience.     

Spatial representation along the proximodistal axis of CA1

CA1 cells receive direct input from space-responsive cells in medial entorhinal cortex (MEC), such as grid cells, as well as more nonspatial cells in lateral entorhinal cortex (LEC). Because MEC projects preferentially to the proximal part of the CA1, bordering CA2, whereas LEC innervates only the distal part, bordering subiculum, we asked if spatial tuning is graded along the transverse axis of CA1. Tetrodes were implanted along the entire proximodistal axis of dorsal CA1 in rats. Data were recorded in cylinders large enough to elicit firing at more than one location in many neurons. Distal CA1 cells showed more dispersed firing and had a larger number of firing fields than proximal cells. Phase-locking of spikes to MEC theta oscillations was weaker in distal CA1 than in proximal CA1. The findings suggest that spatial firing in CA1 is organized transversally, with the strongest spatial modulation occurring in the MEC-associated proximal part.     

Place cells, spatial maps and the population code for memory

The study of population dynamics in hippocampal place cells has emerged as one of the most powerful tools for understanding the encoding, storage and retrieval of declarative memory. Recent work has laid out the contours of an attractor-based hippocampal population code for memory in recurrent circuits of the hippocampus. The code is based on inputs from a topographically organized, path-integration-dependent spatial map that lies upstream in the medial entorhinal cortex. The recurrent networks of the hippocampal formation enable these spatial inputs to be synthesized with nonspatial event-related information.     

The role of competitive learning in the generation of DG fields from EC inputs

We follow up on a suggestion by Rolls and co-workers, that the effects of competitive learning should be assessed on the shape and number of spatial fields that dentate gyrus (DG) granule cells may form when receiving input from medial entorhinal cortex (mEC) grid units. We consider a simple non-dynamical model where DG units are described by a threshold-linear transfer function, and receive feedforward inputs from 1,000 mEC model grid units of various spacing, orientation and spatial phase. Feedforward weights are updated according to a Hebbian rule as the virtual rodent follows a long simulated trajectory through a single environment. Dentate activity is constrained to be very sparse. We find that indeed competitive Hebbian learning tends to result in a few active DG units with a single place field each, rounded in shape and made larger by iterative weight changes. These effects are more pronounced when produced with thousands of DG units and inputs per DG unit, which the realistic system has available, than with fewer units and inputs, in which case several DG units persists with multiple fields. The emergence of single-field units with learning is in contrast, however, to recent data indicating that most active DG units do have multiple fields. We show how multiple irregularly arranged fields can be produced by the addition of non-space selective lateral entorhinal cortex (lEC) units, which are modelled as simply providing an additional effective input specific to each DG unit. The mean number of such multiple DG fields is enhanced, in particular, when lEC and mEC inputs have overall similar variance across DG units. Finally, we show that in a restricted environment the mean size of the fields is unaltered, while their mean number is scaled down with the area of the environment.

Episodic memory on the path to Alzheimer's disease

This review is focused on specific circuits of the medial temporal lobe that have become better understood in recent years for their computational properties contributing to episodic memory and to memory impairment associated with aging and other risk for AD. The layer II neurons in the entorhinal cortex and their targets in the dentate gyrus and CA3 region of hippocampus comprise a system that rapidly encodes representations that are distinct from prior memories. Frank neuron loss in the entorhinal cortex is specific for AD, and related structural and functional changes across the network comprised of the entorhinal cortex and the dentate/CA3 regions hold promise for predicting progression on the path to AD.     

Microcircuits of functionally identified neurons in the rat medial entorhinal cortex

Extracellular recordings have elucidated spatial neural representations without identifying underlying microcircuits. We labeled neurons juxtacellularly in medial entorhinal cortex of freely moving rats with?a friction-based, pipette-stabilization system. In a linear maze novel to the animals, spatial firing of superficial layer neurons was reminiscent of grid cell activity. Layer 2 stellate cells showed stronger theta modulation than layer 3 neurons, and both fired during the ascending phase of field potential theta. Deep-layer neurons showed little or no activity. Layer 2 stellate cells resided in hundreds of small patches. At the dorsomedial entorhinal border, we identified larger (putative parasubicular) patches, which contained polarized head-direction selective neurons firing during the descending theta phase. Three axon systems interconnected patches: centrifugal axons from superficial cells to single large patches, centripetal axons from large-patch cells to single small patches, and circumcurrent axons interconnecting large patches. Our microcircuit analysis during behavior reveals modularity of entorhinal processing. VIDEO ABSTRACT:     

Continuous Attractor Network Model for Conjunctive Position-by-Velocity Tuning of Grid Cells

The spatial responses of many of the cells recorded in layer II of rodent medial entorhinal cortex (MEC) show a triangular grid pattern, which appears to provide an accurate population code for animal spatial position. In layer III, V and VI of the rat MEC, grid cells are also selective to head-direction and are modulated by the speed of the animal. Several putative mechanisms of grid-like maps were proposed, including attractor network dynamics, interactions with theta oscillations or single-unit mechanisms such as firing rate adaptation. In this paper, we present a new attractor network model that accounts for the conjunctive position-by-velocity selectivity of grid cells. Our network model is able to perform robust path integration even when the recurrent connections are subject to random perturbations.     

The mechanism of rate remapping in the dentate gyrus

Rate remapping is a recently revealed neural code in which sensory information modulates the firing rate of hippocampal place cells. The mechanism underlying rate remapping is unknown. Its characteristic modulation, however, must arise from the interaction of the two major inputs to the hippocampus, the medial entorhinal cortex (MEC), in which grid cells represent the spatial position of the rat, and the lateral entorhinal cortex (LEC), in which cells represent the sensory properties of the environment. We have used computational methods to elucidate the mechanism by which this interaction produces rate remapping. We show that the convergence of LEC and MEC inputs, in conjunction with a competitive network process mediated by feedback inhibition, can account quantitatively for this phenomenon. The same principle accounts for why different place fields of the same cell vary independently as sensory information is altered. Our results show that rate remapping can be explained in terms of known mechanisms.     

How environment geometry affects grid cell symmetry and what we can learn from it

The mammalian hippocampal formation provides neuronal representations of environmental location but the underlying mechanisms are unclear. The majority of cells in medial entorhinal cortex and parasubiculum show spatially periodic firing patterns. Grid cells exhibit hexagonal symmetry and form an important subset of this more general class. Occasional changes between hexagonal and non-hexagonal firing patterns imply a common underlying mechanism. Importantly, the symmetrical properties are strongly affected by the geometry of the environment. Here, we introduce a field–boundary interaction model where we demonstrate that the grid cell pattern can be formed from competing place-like and boundary inputs. We show that the modelling results can accurately capture our current experimental observations.