Spatial Coding Dysfunction and Network Instability in the Aging Medial Entorhinal Cortex

Across species, spatial memory declines with age, possibly reflecting altered hippocampal and medial entorhinal cortex (MEC) function. However, the integrity of cellular and network-level spatial coding in aged MEC is unknown. Here, we leveraged in vivo electrophysiology to assess MEC function in young, middle-aged, and aged mice navigating virtual environments. In aged grid cells, we observed impaired stabilization of context-specific spatial firing, correlated with spatial memory deficits. Additionally, aged grid networks shifted firing patterns often but with poor alignment to context changes. Aged spatial firing was also unstable in an unchanging environment. In these same mice, we identified 458 genes differentially expressed with age in MEC, 61 of which had expression correlated with spatial firing stability. These genes were enriched among interneurons and related to synaptic transmission. Together, these findings identify coordinated transcriptomic, cellular, and network changes in MEC implicated in impaired spatial memory in aging.

Subicular neurons encode concave and convex geometries

Animals in the natural world constantly encounter geometrically complex landscapes. Successful navigation requires that they understand geometric features of these landscapes, including boundaries, landmarks, corners and curved areas, all of which collectively define the geometry of the environment1-12. Crucial to the reconstruction of the geometric layout of natural environments are concave and convex features, such as corners and protrusions. However, the neural substrates that could underlie the perception of concavity and convexity in the environment remain elusive. Here we show that the dorsal subiculum contains neurons that encode corners across environmental geometries in an allocentric reference frame. Using longitudinal calcium imaging in freely behaving mice, we find that corner cells tune their activity to reflect the geometric properties of corners, including corner angles, wall height and the degree of wall intersection. A separate population of subicular neurons encode convex corners of both larger environments and discrete objects. Both corner cells are non-overlapping with the population of subicular neurons that encode environmental boundaries. Furthermore, corner cells that encode concave or convex corners generalize their activity such that they respond, respectively, to concave or convex curvatures within an environment. Together, our findings suggest that the subiculum contains the geometric information needed to reconstruct the shape and layout of naturalistic spatial environments.

Parahippocampal neurons encode task-relevant information for goal-directed navigation

A behavioral strategy crucial to survival is directed navigation to a goal, such as a food or home location. One potential neural substrate for supporting goal-directed navigation is the parahippocampus, which contains neurons that represent an animal's position, orientation, and movement through the world, and that change their firing activity to encode behaviorally relevant variables such as reward. However, little prior work on the parahippocampus has considered how neurons encode variables during goal-directed navigation in environments that dynamically change. Here, we recorded single units from rat parahippocampal cortex while subjects performed a goal-directed task. The maze dynamically changed goal-locations via a visual cue on a trial-to-trial basis, requiring subjects to use cue-location associations to receive reward. We observed a mismatch-like signal, with elevated neural activity on incorrect trials, leading to rate-remapping. The strength of this remapping correlated with task performance. Recordings during open-field foraging allowed us to functionally define navigational coding for a subset of the neurons recorded in the maze. This approach revealed that head-direction coding units remapped more than other functional-defined units. Taken together, this work thus raises the possibility that during goal-directed navigation, parahippocampal neurons encode error information reflective of an animal's behavioral performance.

Hippocampal sequences span experience relative to rewards

Hippocampal place cells fire in sequences that span spatial environments and non-spatial modalities, suggesting that hippocampal activity can anchor to the most behaviorally salient aspects of experience. As reward is a highly salient event, we hypothesized that sequences of hippocampal activity can anchor to rewards. To test this, we performed two-photon imaging of hippocampal CA1 neurons as mice navigated virtual environments with changing hidden reward locations. When the reward moved, the firing fields of a subpopulation of cells moved to the same relative position with respect to reward, constructing a sequence of reward-relative cells that spanned the entire task structure. The density of these reward-relative sequences increased with task experience as additional neurons were recruited to the reward-relative population. Conversely, a largely separate subpopulation maintained a spatially-based place code. These findings thus reveal separate hippocampal ensembles can flexibly encode multiple behaviorally salient reference frames, reflecting the structure of the experience.

Hippocampal place code plasticity in CA1 requires postsynaptic membrane fusion

Rapid delivery of glutamate receptors to the postsynaptic membrane via vesicle fusion is a central component of synaptic plasticity. However, it is unknown how this process supports specific neural computations during behavior. To bridge this gap, we combined conditional genetic deletion of a component of the postsynaptic membrane fusion machinery, Syntaxin3 (Stx3), in hippocampal CA1 neurons of mice with population in vivo calcium imaging. This approach revealed that Stx3 is necessary for forming the neural dynamics that support novelty processing, spatial reward memory and offline memory consolidation. In contrast, CA1 Stx3 was dispensable for maintaining aspects of the neural code that exist presynaptic to CA1 such as representations of context and space. Thus, manipulating postsynaptic membrane fusion identified computations that specifically require synaptic restructuring via membrane trafficking in CA1 and distinguished them from neural representation that could be inherited from upstream brain regions or learned through other mechanisms.

Ketamine evoked disruption of entorhinal and hippocampal spatial maps

Ketamine, a rapid-acting anesthetic and acute antidepressant, carries undesirable spatial cognition side effects including out-of-body experiences and spatial memory impairments. The neural substrates that underlie these alterations in spatial cognition however, remain incompletely understood. Here, we used electrophysiology and calcium imaging to examine ketamine's impacts on the medial entorhinal cortex and hippocampus, which contain neurons that encode an animal's spatial position, as mice navigated virtual reality and real world environments. Ketamine acutely increased firing rates, degraded cell-pair temporal firing-rate relationships, and altered oscillations, leading to longer-term remapping of spatial representations. In the reciprocally connected hippocampus, the activity of neurons that encode the position of the animal was suppressed after ketamine administration. Together, these findings demonstrate ketamine-induced dysfunction of the MEC-hippocampal circuit at the single cell, local-circuit population, and network levels, connecting previously demonstrated physiological effects of ketamine on spatial cognition to alterations in the spatial navigation circuit.

Remapping in a recurrent neural network model of navigation and context inference

Neurons in navigational brain regions provide information about position, orientation, and speed relative to environmental landmarks. These cells also change their firing patterns ('remap') in response to changing contextual factors such as environmental cues, task conditions, and behavioral states, which influence neural activity throughout the brain. How can navigational circuits preserve their local computations while responding to global context changes? To investigate this question, we trained recurrent neural network models to track position in simple environments while at the same time reporting transiently-cued context changes. We show that these combined task constraints (navigation and context inference) produce activity patterns that are qualitatively similar to population-wide remapping in the entorhinal cortex, a navigational brain region. Furthermore, the models identify a solution that generalizes to more complex navigation and inference tasks. We thus provide a simple, general, and experimentally-grounded model of remapping as one neural circuit performing both navigation and context inference.

A positively tuned voltage indicator for extended electrical recordings in the brain

Genetically encoded voltage indicators (GEVIs) enable optical recording of electrical signals in the brain, providing subthreshold sensitivity and temporal resolution not possible with calcium indicators. However, one- and two-photon voltage imaging over prolonged periods with the same GEVI has not yet been demonstrated. Here, we report engineering of ASAP family GEVIs to enhance photostability by inversion of the fluorescence-voltage relationship. Two of the resulting GEVIs, ASAP4b and ASAP4e, respond to 100-mV depolarizations with ≥180% fluorescence increases, compared with the 50% fluorescence decrease of the parental ASAP3. With standard microscopy equipment, ASAP4e enables single-trial detection of spikes in mice over the course of minutes. Unlike GEVIs previously used for one-photon voltage recordings, ASAP4b and ASAP4e also perform well under two-photon illumination. By imaging voltage and calcium simultaneously, we show that ASAP4b and ASAP4e can identify place cells and detect voltage spikes with better temporal resolution than commonly used calcium indicators. Thus, ASAP4b and ASAP4e extend the capabilities of voltage imaging to standard one- and two-photon microscopes while improving the duration of voltage recordings.

Ketamine evoked disruption of entorhinal and hippocampal spatial maps

Ketamine, a rapid-acting anesthetic and acute antidepressant, carries undesirable spatial cognition side effects including out-of-body experiences and spatial memory impairments. The neural substrates that underlie these alterations in spatial cognition however, remain incompletely understood. Here, we used electrophysiology and calcium imaging to examine ketamine's impacts on the medial entorhinal cortex and hippocampus, which contain neurons that encode an animal's spatial position, as mice navigated virtual reality and real world environments. Ketamine induced an acute disruption and long-term re-organization of entorhinal spatial representations. This acute ketamine-induced disruption reflected increased excitatory neuron firing rates and degradation of cell-pair temporal firing rate relationships. In the reciprocally connected hippocampus, the activity of neurons that encode the position of the animal was suppressed after ketamine administration. Together, these findings point to disruption in the spatial coding properties of the entorhinal-hippocampal circuit as a potential neural substrate for ketamine-induced changes in spatial cognition.

Neural ensembles in navigation: From single cells to population codes

The brain can represent behaviorally relevant information through the firing of individual neurons as well as the coordinated firing of ensembles of neurons. Neurons in the hippocampus and associated cortical regions participate in a variety of types of ensembles to support navigation. These ensemble types include single cell codes, population codes, time-compressed sequences, behavioral sequences, and engrams. We present the physiological basis and behavioral relevance of ensemble firing. We discuss how these traditional definitions of ensembles can constrain or expand potential analyses due to the underlying assumptions and abstractions made. We highlight how coding can change at the ensemble level while underlying single cell codes remain intact. Finally, we present how ensemble definitions could be broadened to better understand the full complexity of the brain.

Remapping in a recurrent neural network model of navigation and context inference

Neurons in navigational brain regions provide information about position, orientation, and speed relative to environmental landmarks. These cells also change their firing patterns ("remap") in response to changing contextual factors such as environmental cues, task conditions, and behavioral state, which influence neural activity throughout the brain. How can navigational circuits preserve their local computations while responding to global context changes? To investigate this question, we trained recurrent neural network models to track position in simple environments while at the same time reporting transiently-cued context changes. We show that these combined task constraints (navigation and context inference) produce activity patterns that are qualitatively similar to population-wide remapping in the entorhinal cortex, a navigational brain region. Furthermore, the models identify a solution that generalizes to more complex navigation and inference tasks. We thus provide a simple, general, and experimentally-grounded model of remapping as one neural circuit performing both navigation and context inference.

A unified theory for the computational and mechanistic origins of grid cells

The discovery of entorhinal grid cells has generated considerable interest in how and why hexagonal firing fields might emerge in a generic manner from neural circuits, and what their computational significance might be. Here, we forge a link between the problem of path integration and the existence of hexagonal grids, by demonstrating that such grids arise in neural networks trained to path integrate under simple biologically plausible constraints. Moreover, we develop a unifying theory for why hexagonal grids are ubiquitous in path-integrator circuits. Such trained networks also yield powerful mechanistic hypotheses, exhibiting realistic levels of biological variability not captured by hand-designed models. We furthermore develop methods to analyze the connectome and activity maps of our networks to elucidate fundamental mechanisms underlying path integration. These methods provide a road map to go from connectomic and physiological measurements to conceptual understanding in a manner that could generalize to other settings.

Neural circuit dynamics of drug-context associative learning in the mouse hippocampus

The environmental context associated with previous drug consumption is a potent trigger for drug relapse. However, the mechanism by which neural representations of context are modified to incorporate information associated with drugs of abuse remains unknown. Using longitudinal calcium imaging in freely behaving mice, we find that unlike the associative learning of natural reward, drug-context associations for psychostimulants and opioids are encoded in a specific subset of hippocampal neurons. After drug conditioning, these neurons weakened their spatial coding for the non-drug paired context, resulting in an orthogonal representation for the drug versus non-drug context that was predictive of drug-seeking behavior. Furthermore, these neurons were selected based on drug-spatial experience and were exclusively tuned to animals' allocentric position. Together, this work reveals how drugs of abuse alter the hippocampal circuit to encode drug-context associations and points to the possibility of targeting drug-associated memory in the hippocampus.

Distance-tuned neurons drive specialized path integration calculations in medial entorhinal cortex

During navigation, animals estimate their position using path integration and landmarks, engaging many brain areas. Whether these areas follow specialized or universal cue integration principles remains incompletely understood. We combine electrophysiology with virtual reality to quantify cue integration across thousands of neurons in three navigation-relevant areas: primary visual cortex (V1), retrosplenial cortex (RSC), and medial entorhinal cortex (MEC). Compared with V1 and RSC, path integration influences position estimates more in MEC, and conflicts between path integration and landmarks trigger remapping more readily. Whereas MEC codes position prospectively, V1 codes position retrospectively, and RSC is intermediate between the two. Lowered visual contrast increases the influence of path integration on position estimates only in MEC. These properties are most pronounced in a population of MEC neurons, overlapping with grid cells, tuned to distance run in darkness. These results demonstrate the specialized role that path integration plays in MEC compared with other navigation-relevant cortical areas.

Dynamic and reversible remapping of network representations in an unchanging environment

Neurons in the medial entorhinal cortex alter their firing properties in response to environmental changes. This flexibility in neural coding is hypothesized to support navigation and memory by dividing sensory experience into unique episodes. However, it is unknown how the entorhinal circuit as a whole transitions between different representations when sensory information is not delineated into discrete contexts. Here we describe rapid and reversible transitions between multiple spatial maps of an unchanging task and environment. These remapping events were synchronized across hundreds of neurons, differentially affected navigational cell types, and correlated with changes in running speed. Despite widespread changes in spatial coding, remapping comprised a translation along a single dimension in population-level activity space, enabling simple decoding strategies. These findings provoke reconsideration of how the medial entorhinal cortex dynamically represents space and suggest a remarkable capacity of cortical circuits to rapidly and substantially reorganize their neural representations.

Spatial memory: Place cell activity is causally related to behavior

A recent study using new holographic optogenetic stimulation technology has provided direct evidence that hippocampal place cell activity is sufficient to drive memory and navigation-related behaviors.

Topography in the Bursting Dynamics of Entorhinal Neurons

Medial entorhinal cortex contains neural substrates for representing space. These substrates include grid cells that fire in repeating locations and increase in scale progressively along the dorsal-to-ventral entorhinal axis, with the physical distance between grid firing nodes increasing from tens of centimeters to several meters in rodents. Whether the temporal scale of grid cell spiking dynamics shows a similar dorsal-to-ventral organization remains unknown. Here, we report the presence of a dorsal-to-ventral gradient in the temporal spiking dynamics of grid cells in behaving mice. This gradient in bursting supports the emergence of a dorsal grid cell population with a high signal-to-noise ratio. In vitro recordings combined with a computational model point to a role for gradients in non-inactivating sodium conductances in supporting the bursting gradient in vivo. Taken together, these results reveal a complementary organization in the temporal and intrinsic properties of entorhinal cells.

Mouse entorhinal cortex encodes a diverse repertoire of self-motion signals

Neural circuits generate representations of the external world from multiple information streams. The navigation system provides an exceptional lens through which we may gain insights about how such computations are implemented. Neural circuits in the medial temporal lobe construct a map-like representation of space that supports navigation. This computation integrates multiple sensory cues, and, in addition, is thought to require cues related to the individual's movement through the environment. Here, we identify multiple self-motion signals, related to the position and velocity of the head and eyes, encoded by neurons in a key node of the navigation circuitry of mice, the medial entorhinal cortex (MEC). The representation of these signals is highly integrated with other cues in individual neurons. Such information could be used to compute the allocentric location of landmarks from visual cues and to generate internal representations of space.

Entorhinal velocity signals reflect environmental geometry

The entorhinal cortex contains neurons that represent self-location, including grid cells that fire in periodic locations and velocity signals that encode running speed and head direction. Although the size and shape of the environment influence grid patterns, whether entorhinal velocity signals are equally influenced or provide a universal metric for self-motion across environments remains unknown. Here we report that speed cells rescale after changes to the size and shape of the environment. Moreover, head direction cells reorganize in an experience-dependent manner to align with the axis of environmental change. A knockout mouse model allows dissociation of the coordination between cell types, with grid and speed cells, but not head direction cells, responding in concert to environmental change. These results point to malleability in the coding features of multiple entorhinal cell types and have implications for which cell types contribute to the velocity signal used by computational models of grid cells.

The Shifting Sands of Cortical Divisions

In this issue of Neuron, a new study by Minderer et al. (2019) examines the activity of thousands of cortical neurons during a navigation task and reveals that features of the task encoded by neurons vary smoothly across cortex rather than falling into functionally discrete cortical regions.

Remembered reward locations restructure entorhinal spatial maps

Ethologically relevant navigational strategies often incorporate remembered reward locations. Although neurons in the medial entorhinal cortex provide a maplike representation of the external spatial world, whether this map integrates information regarding learned reward locations remains unknown. We compared entorhinal coding in rats during a free-foraging task and a spatial memory task. Entorhinal spatial maps restructured to incorporate a learned reward location, which in turn improved positional decoding near this location. This finding indicates that different navigational strategies drive the emergence of discrete entorhinal maps of space and points to a role for entorhinal codes in a diverse range of navigational behaviors.

Cell types for our sense of location: where we are and where we are going

Technological advances in profiling cells along genetic, anatomical and physiological axes have fomented interest in identifying all neuronal cell types. This goal nears completion in specialized circuits such as the retina, while remaining more elusive in higher order cortical regions. We propose that this differential success of cell type identification may not simply reflect technological gaps in co-registering genetic, anatomical and physiological features in the cortex. Rather, we hypothesize it reflects evolutionarily driven differences in the computational principles governing specialized circuits versus more general-purpose learning machines. In this framework, we consider the question of cell types in medial entorhinal cortex (MEC), a region likely to be involved in memory and navigation. While MEC contains subsets of identifiable functionally defined cell types, recent work employing unbiased statistical methods and more diverse tasks reveals unsuspected heterogeneity and adaptivity in MEC firing patterns. This suggests MEC may operate more as a generalist circuit, obeying computational design principles resembling those governing other higher cortical regions.

Self-motion processing in visual and entorhinal cortices: inputs, integration, and implications for position coding

The sensory signals generated by self-motion are complex and multimodal, but the ability to integrate these signals into a unified self-motion percept to guide navigation is essential for animal survival. Here, we summarize classic and recent work on self-motion coding in the visual and entorhinal cortices of the rodent brain. We compare motion processing in rodent and primate visual cortices, highlighting the strengths of classic primate work in establishing causal links between neural activity and perception, and discuss the integration of motor and visual signals in rodent visual cortex. We then turn to the medial entorhinal cortex (MEC), where calculations using self-motion to update position estimates are thought to occur. We focus on several key sources of self-motion information to MEC: the medial septum, which provides locomotor speed information; visual cortex, whose input has been increasingly recognized as essential to both position and speed-tuned MEC cells; and the head direction system, which is a major source of directional information for self-motion estimates. These inputs create a large and diverse group of self-motion codes in MEC, and great interest remains in how these self-motion codes might be integrated by MEC grid cells to estimate position. However, which signals are used in these calculations and the mechanisms by which they are integrated remain controversial. We end by proposing future experiments that could further our understanding of the interactions between MEC cells that code for self-motion and position and clarify the relationship between the activity of these cells and spatial perception.

Heterogeneity in hippocampal place coding

The discovery of place cells provided fundamental insight into the neural basis by which the hippocampus encodes spatial memories and supports navigation and prompted the development of computational models to explain the emergence of their spatial selectively. Many such works posit that input from entorhinal grid cells is critical to the formation of place fields, a prediction that has received mixed experimental support. Potentially reconciling seemingly conflicting findings is recent work indicating that subpopulations of pyramidal neurons are functionally distinct and may be driven to varying degrees by different inputs. Additionally, new studies have demonstrated that hippocampal principal neurons encode a myriad of features extending beyond current position. Here, we highlight recent evidence for how extensive heterogeneity in connectivity and genetic expression could interact with membrane biophysics to enable place cells to encode a diverse range of stimuli. These recent findings highlight the need for more computational models that integrate these heterogeneous features of hippocampal principal neurons.

A Multiplexed, Heterogeneous, and Adaptive Code for Navigation in Medial Entorhinal Cortex

Medial entorhinal grid cells display strikingly symmetric spatial firing patterns. The clarity of these patterns motivated the use of specific activity pattern shapes to classify entorhinal cell types. While this approach successfully revealed cells that encode boundaries, head direction, and running speed, it left a majority of cells unclassified, and its pre-defined nature may have missed unconventional, yet important coding properties. Here, we apply an unbiased statistical approach to search for cells that encode navigationally relevant variables. This approach successfully classifies the majority of entorhinal cells and reveals unsuspected entorhinal coding principles. First, we find a high degree of mixed selectivity and heterogeneity in superficial entorhinal neurons. Second, we discover a dynamic and remarkably adaptive code for space that enables entorhinal cells to rapidly encode navigational information accurately at high running speeds. Combined, these observations advance our current understanding of the mechanistic origins and functional implications of the entorhinal code for navigation. VIDEO ABSTRACT.

Environmental boundaries as a mechanism for correcting and anchoring spatial maps

Ubiquitous throughout the animal kingdom, path integration-based navigation allows an animal to take a circuitous route out from a home base and using only self-motion cues, calculate a direct vector back. Despite variation in an animal's running speed and direction, medial entorhinal grid cells fire in repeating place-specific locations, pointing to the medial entorhinal circuit as a potential neural substrate for path integration-based spatial navigation. Supporting this idea, grid cells appear to provide an environment-independent metric representation of the animal's location in space and preserve their periodic firing structure even in complete darkness. However, a series of recent experiments indicate that spatially responsive medial entorhinal neurons depend on environmental cues in a more complex manner than previously proposed. While multiple types of landmarks may influence entorhinal spatial codes, environmental boundaries have emerged as salient landmarks that both correct error in entorhinal grid cells and bind internal spatial representations to the geometry of the external spatial world. The influence of boundaries on error correction and grid symmetry points to medial entorhinal border cells, which fire at a high rate only near environmental boundaries, as a potential neural substrate for landmark-driven control of spatial codes. The influence of border cells on other entorhinal cell populations, such as grid cells, could depend on plasticity, raising the possibility that experience plays a critical role in determining how external cues influence internal spatial representations.

Spatial representation: maps of fragmented space

Grid cells in medial entorhinal cortex are thought to act as a neural metric for spatial navigation. A new study has examined the ability of grid cells to use self-motion cues to form a global map across fragmented spaces.

Environmental boundaries as an error correction mechanism for grid cells

Medial entorhinal grid cells fire in periodic, hexagonally patterned locations and are proposed to support path-integration-based navigation. The recursive nature of path integration results in accumulating error and, without a corrective mechanism, a breakdown in the calculation of location. The observed long-term stability of grid patterns necessitates that the system either performs highly precise internal path integration or implements an external landmark-based error correction mechanism. To distinguish these possibilities, we examined grid cells in behaving rodents as they made long trajectories across an open arena. We found that error accumulates relative to time and distance traveled since the animal last encountered a boundary. This error reflects coherent drift in the grid pattern. Further, interactions with boundaries yield direction-dependent error correction, suggesting that border cells serve as a neural substrate for error correction. These observations, combined with simulations of an attractor network grid cell model, demonstrate that landmarks are crucial to grid stability.

Large scale in vivo recordings to study neuronal biophysics

Over the last several years, technological advances have enabled researchers to more readily observe single-cell membrane biophysics in awake, behaving animals. Studies utilizing these technologies have provided important insights into the mechanisms generating functional neural codes in both sensory and non-sensory cortical circuits. Crucial for a deeper understanding of how membrane biophysics control circuit dynamics however, is a continued effort to move toward large scale studies of membrane biophysics, in terms of the numbers of neurons and ion channels examined. Future work faces a number of theoretical and technical challenges on this front but recent technological developments hold great promise for a larger scale understanding of how membrane biophysics contribute to circuit coding and computation.

Spatial representation: maps in a temporal void

It has been suggested that the matrix-like firing structure of entorhinal grid cells is caused by interference between membrane oscillations at slightly different theta frequencies. A recent report suggests that grid signals can be generated in the absence of theta oscillations.

Computational models of grid cells

Grid cells are space-modulated neurons with periodic firing fields. In moving animals, the multiple firing fields of an individual grid cell form a triangular pattern tiling the entire space available to the animal. Collectively, grid cells are thought to provide a context-independent metric representation of the local environment. Since the discovery of grid cells in 2005, a number of models have been proposed to explain the formation of spatially repetitive firing patterns as well as the conversion of these signals to place signals one synapse downstream in the hippocampus. The present article reviews the most recent developments in our understanding of how grid patterns are generated, maintained, and transformed, with particular emphasis on second-generation computational models that have emerged during the past 2-3 years in response to criticism and new data.

Phase precession and variable spatial scaling in a periodic attractor map model of medial entorhinal grid cells with realistic after-spike dynamics

We present a model that describes the generation of the spatial (grid fields) and temporal (phase precession) properties of medial entorhinal cortical (MEC) neurons by combining network and intrinsic cellular properties. The model incorporates network architecture derived from earlier attractor map models, and is implemented in 1D for simplicity. Periodic driving of conjunctive (position × head-direction) layer-III MEC cells at theta frequency with intensity proportional to the rat's speed, moves an 'activity bump' forward in network space at a corresponding speed. The addition of prolonged excitatory currents and simple after-spike dynamics resembling those observed in MEC stellate cells (for which new data are presented) accounts for both phase precession and the change in scale of grid fields along the dorso-ventral axis of MEC. Phase precession in the model depends on both synaptic connectivity and intrinsic currents, each of which drive neural spiking either during entry into, or during exit out of a grid field. Thus, the model predicts that the slope of phase precession changes between entry into and exit out of the field. The model also exhibits independent variation in grid spatial period and grid field size, which suggests possible experimental tests of the model.

Cholinergic modulation of the resonance properties of stellate cells in layer II of medial entorhinal cortex

In vitro whole cell patch-clamp recordings of stellate cells in layer II of medial entorhinal cortex show a subthreshold membrane potential resonance in response to a sinusoidal current injection of varying frequency. Physiological recordings from awake behaving animals show that neurons in layer II medial entorhinal cortex, termed "grid cells," fire in a spatially selective manner such that each cell's multiple firing fields form a hexagonal grid. Both the spatial periodicity of the grid fields and the resonance frequency change systematically in neurons along the dorsal to ventral axis of medial entorhinal cortex. Previous work has also shown that grid field spacing and acetylcholine levels change as a function of the novelty to a particular environment. Using in vitro whole cell patch-clamp recordings, our study shows that both resonance frequency and resonance strength vary as a function of cholinergic modulation. Furthermore, our data suggest that these changes in resonance properties are mediated through modulation of h-current and m-current.

Cellular dynamical mechanisms for encoding the time and place of events along spatiotemporal trajectories in episodic memory

Understanding the mechanisms of episodic memory requires linking behavioral data and lesion effects to data on the dynamics of cellular membrane potentials and population interactions within brain regions. Linking behavior to specific membrane channels and neurochemicals has implications for therapeutic applications. Lesions of the hippocampus, entorhinal cortex and subcortical nuclei impair episodic memory function in humans and animals, and unit recording data from these regions in behaving animals indicate episodic memory processes. Intracellular recording in these regions demonstrates specific cellular properties including resonance, membrane potential oscillations and bistable persistent spiking that could underlie the encoding and retrieval of episodic trajectories. A model presented here shows how intrinsic dynamical properties of neurons could mediate the encoding of episodic memories as complex spatiotemporal trajectories. The dynamics of neurons allow encoding and retrieval of unique episodic trajectories in multiple continuous dimensions including temporal intervals, personal location, the spatial coordinates and sensory features of perceived objects and generated actions, and associations between these elements. The model also addresses how cellular dynamics could underlie unit firing data suggesting mechanisms for coding continuous dimensions of space, time, sensation and action.

Evaluation of the oscillatory interference model of grid cell firing through analysis and measured period variance of some biological oscillators

Models of the hexagonally arrayed spatial activity pattern of grid cell firing in the literature generally fall into two main categories: continuous attractor models or oscillatory interference models. Burak and Fiete (2009, PLoS Comput Biol) recently examined noise in two continuous attractor models, but did not consider oscillatory interference models in detail. Here we analyze an oscillatory interference model to examine the effects of noise on its stability and spatial firing properties. We show analytically that the square of the drift in encoded position due to noise is proportional to time and inversely proportional to the number of oscillators. We also show there is a relatively fixed breakdown point, independent of many parameters of the model, past which noise overwhelms the spatial signal. Based on this result, we show that a pair of oscillators are expected to maintain a stable grid for approximately t = 5µ³/(4πσ)² seconds where µ is the mean period of an oscillator in seconds and σ² its variance in seconds². We apply this criterion to recordings of individual persistent spiking neurons in postsubiculum (dorsal presubiculum) and layers III and V of entorhinal cortex, to subthreshold membrane potential oscillation recordings in layer II stellate cells of medial entorhinal cortex and to values from the literature regarding medial septum theta bursting cells. All oscillators examined have expected stability times far below those seen in experimental recordings of grid cells, suggesting the examined biological oscillators are unfit as a substrate for current implementations of oscillatory interference models. However, oscillatory interference models can tolerate small amounts of noise, suggesting the utility of circuit level effects which might reduce oscillator variability. Further implications for grid cell models are discussed.

For many animals, including rats, accurate spatial memory over relatively large areas is important in order to find food and shelter. Just as unique points in time can be efficiently represented by combinations of repeating elements like hours, days, and months, points in space can be represented as combinations of elements that repeat at different spatial scales. Just such a code has been identified in the brains of rats and it shows an intriguing triangular spacing of encoded locations. Two different explanations have been developed as to what general mechanism in the brain might be able to generate this unusual code. However, to date there is not conclusive experimental evidence indicating whether either of the two explanations is correct. Here we show in detail that one of the explanations, called oscillatory interference, has specific requirements regarding the amount of variability in the system that implements it. We then report data experimentally examining candidate systems to evaluate their levels of noise. The large amount of noise that we find presents a challenge to the currently suggested biological implementations of oscillatory interference, but it does not provide support for the alternative explanation.

A phase code for memory could arise from circuit mechanisms in entorhinal cortex

Neurophysiological data reveals intrinsic cellular properties that suggest how entorhinal cortical neurons could code memory by the phase of their firing. Potential cellular mechanisms for this phase coding in models of entorhinal function are reviewed. This mechanism for phase coding provides a substrate for modeling the responses of entorhinal grid cells, as well as the replay of neural spiking activity during waking and sleep. Efforts to implement these abstract models in more detailed biophysical compartmental simulations raise specific issues that could be addressed in larger scale population models incorporating mechanisms of inhibition.

Computation by oscillations: implications of experimental data for theoretical models of grid cells

Recordings in awake, behaving animals demonstrate that cells in medial entorhinal cortex (mEC) show "grid cell" firing activity when a rat explores an open environment. Intracellular recording in slices from different positions along the dorsal to ventral axis show differences in intrinsic properties such as subthreshold membrane potential oscillations (MPO), resonant frequency, and the presence of the hyperpolarization-activated cation current (h-current). The differences in intrinsic properties correlate with differences in grid cell spatial scale along the dorsal-ventral axis of mEC. Two sets of computational models have been proposed to explain the grid cell firing phenomena: oscillatory interference models and attractor-dynamic models. Both types of computational models are briefly reviewed, and cellular experimental evidence is interpreted and presented in the context of both models. The oscillatory interference model has variations that include an additive model and a multiplicative model. Experimental data on the voltage-dependence of oscillations presented here support the additive model. The additive model also simulates data from ventral neurons showing large spacing between grid firing fields within the limits of observed MPO frequencies. The interactions of h-current with synaptic modification suggest that the difference in intrinsic properties could also contribute to differences in grid cell properties due to attractor dynamics along the dorsal to ventral axis of mEC. Mechanisms of oscillatory interference and attractor dynamics may make complementary contributions to the properties of grid cell firing in entorhinal cortex.

Temporal frequency of subthreshold oscillations scales with entorhinal grid cell field spacing

Grid cells in layer II of rat entorhinal cortex fire to spatial locations in a repeating hexagonal grid, with smaller spacing between grid fields for neurons in more dorsal anatomical locations. Data from in vitro whole-cell patch recordings showed differences in frequency of subthreshold membrane potential oscillations in entorhinal neurons that correspond to different positions along the dorsal-to-ventral axis, supporting a model of physiological mechanisms for grid cell responses.

Cholinergic modulation of cortical function

Extensive physiological research has demonstrated a number of common effects of acetylcholine within cortical structures, including the hippocampus, piriform cortex, and neocortex (Hasselmo, 1995, 1999). This article will provide a description of how the different physiological effects of acetylcholine could interact to alter specific functional properties of the cortex. The physiological effects of acetylcholine serve to enhance the influence of feed- forward afferent input to the cortex while decreasing background activity by suppressing excitatory feedback connections within cortical circuits. By enhancing the response to sensory input, high levels of acetylcholine enhance attention to sensory stimuli in the environment and enhance encoding of memory for specific stimuli. Interference from prior memory is reduced by suppressing synapses modified by prior learning (Sevilla et al., 2002; Linster et al., 2003).

Difference in time course of modulation of synaptic transmission by group II versus group III metabotropic glutamate receptors in region CA1 of the hippocampus

We investigated the time course of modulation of synaptic transmission by group II and group III metabotropic glutamate receptors in region CA1 of the hippocampus. In the presence of 50 microM picrotoxin, pressure pulse application of 1 mM glutamate resulted in a fast onset of suppression of synaptic transmission in stratum lacunosum moleculare and a slower onset of suppression in stratum radiatum, with both effects returning to baseline over the course of several minutes. Application of 50 microM of the group II agonist (2R,4R)-APDC in stratum lacunosum moleculare resulted in the same fast onset of suppression while having no effect in stratum radiatum. Pressure pulse application of 100 microM DL-AP4 in stratum lacunosum moleculare and stratum radiatum resulted in a much slower onset of suppression of synaptic transmission than (2R,4R)-APDC. Suppression by (2R,4R)-APDC was accompanied by a rapid enhancement of paired pulse facilitation, indicative of a presynaptic mechanism. This demonstrates that activation of group II mGluRs in the hippocampus causes a fast onset of suppression in stratum lacunosum moleculare, while activation of group III mGluRs causes a slower onset of suppression. The difference in time course for group II vs. group III mGluRs suggests a different functional role, with group II playing a potential role in making synapses act as low pass filters.

Grid cell firing may arise from interference of theta frequency membrane potential oscillations in single neurons

Intracellular recording and computational modelling suggest that interactions of subthreshold membrane potential oscillation frequency in different dendritic branches of entorhinal cortex stellate cells could underlie the functional coding of continuous dimensions of space and time. Among other things, these interactions could underlie properties of grid cell field spacing. The relationship between experimental data on membrane potential oscillation frequency (f) and grid cell field spacing (G) indicates a constant scaling factor H = fG. This constant scaling factor between temporal oscillation frequency and spatial periodicity provides a starting constraint that is used to derive the model of Burgess et al. (Hippocampus, 2007). This model provides a consistent quantitative link between single cell physiological properties and properties of spiking units in awake behaving animals. Further properties and predictions of this model about single cell and network physiological properties are analyzed. In particular, the model makes quantitative predictions about the change in membrane potential, single cell oscillation frequency, and network oscillation frequency associated with speed of movement, about the independence of single cell properties from network theta rhythm oscillations, and about the effect of variations in initial oscillatory phase on the pattern of grid cell firing fields. These same mechanisms of subthreshold oscillations may play a more general role in memory function, by providing a method for learning arbitrary time intervals in memory sequences.

Muscarinic suppression in stratum radiatum of CA1 shows dependence on presynaptic M1 receptors and is not dependent on effects at GABAB receptors

Cholinergic modulation of synaptic transmission is vital to memory processes and may be responsible for setting network dynamics in the hippocampus appropriate for encoding of information. found evidence suggesting M1 receptors cause presynaptic inhibition of glutamatergic transmission, while research supports a role of the M2 receptor. We examined muscarinic inhibition of fEPSPs in stratum radiatum of mice lacking m1 subtype receptors (KO) compared to wild type (WT) controls. WT mice exhibit greater suppression of transmission by muscarine as compared to KO in a dose dependent fashion. Oxotremorine shows no significant difference in suppression between WT and KO, while MCN-A-343, an M1 agonist, exhibits a significant difference between KO and WT, with KO showing no suppression. One hundred micromolar SGS-742, a selective GABA(B) antagonist, fails to affect either normal transmission or muscarinic suppression in either WT or KO suggesting that differences in suppression between the groups is not attributable to differences in GABA(B) receptor activation due to muscarinic activation of GABAergic interneurons. These findings support a role for presynaptic m1 mAChRs in modulation of synaptic transmission in CA1, but indicate that other muscarinic receptor subtypes, such as M2, are also involved in suppression of synaptic potentials.

Nicotinic modulation of glutamatergic synaptic transmission in region CA3 of the hippocampus

Cholinergic modulation of synaptic transmission in the hippocampus appears to be involved in learning, memory and attentional processes. In brain slice preparations of hippocampal region CA3, we have explored the effect of nicotine on the afferent connections of stratum lacunosum moleculare (SLM) vs. the intrinsic connections of stratum radiatum (SR). Nicotine application had a lamina-selective effect, causing changes in synaptic transmission only in SLM. The nicotinic effect in SLM was characterized by a transient decrease in synaptic potential size followed by a longer period of enhancement of synaptic transmission. The effect was blocked by gamma-aminobutyric acid (GABA)ergic antagonists, indicating the role of GABAergic interneurons in the observed nicotinic effect. The biphasic nature of the nicotinic effect could be due to a difference in receptor subtypes, as supported by the effects of the nicotinic antagonists mecamylamine and methyllycaconitine. Nicotinic modulation of glutamatergic synaptic transmission could complement muscarinic suppression of intrinsic connections, amplifying incoming information and providing a physiological mechanism for the memory-enhancing effect of nicotine.