Grid cells and neural coding in high-end cortices

Prefrontal cortical ripples mediate top-down suppression of hippocampal reactivation during sleep memory consolidation

Consolidation of initially encoded hippocampal representations in the neocortex through reactivation is crucial for long-term memory formation and is facilitated by the coordination of hippocampal sharp-wave ripples (SWRs) with cortical slow and spindle oscillations during non-REM sleep. Recent evidence suggests that high-frequency cortical ripples can also coordinate with hippocampal SWRs in support of consolidation; however, the contribution of cortical ripples to reactivation remains unclear. We used high-density, continuous recordings in the hippocampus (area CA1) and prefrontal cortex (PFC) over the course of spatial learning and show that independent PFC ripples dissociated from SWRs are prevalent in NREM sleep and predominantly suppress hippocampal activity. PFC ripples paradoxically mediate top-down suppression of hippocampal reactivation rather than coordination, and this suppression is stronger for assemblies that are reactivated during coordinated CA1-PFC ripples for consolidation of recent experiences. Further, we show non-canonical, serial coordination of independent cortical ripples with slow and spindle oscillations, which are known signatures of memory consolidation. These results establish a role for prefrontal cortical ripples in top-down regulation of behaviorally relevant hippocampal representations during consolidation.

Functional states of prelimbic and related circuits during the acquisition of a GO/noGO task in rats

GO/noGO tasks enable assessing decision-making processes and the ability to suppress a specific action according to the context. Here, rats had to discriminate between 2 visual stimuli (GO or noGO) shown on an iPad screen. The execution (for GO) or nonexecution (for noGO) of the selected action (to touch or not the visual display) were reinforced with food. The main goal was to record and to analyze local field potentials collected from cortical and subcortical structures when the visual stimuli were shown on the touch screen and during the subsequent activities. Rats were implanted with recording electrodes in the prelimbic cortex, primary motor cortex, nucleus accumbens septi, basolateral amygdala, dorsolateral and dorsomedial striatum, hippocampal CA1, and mediodorsal thalamic nucleus. Spectral analyses of the collected data demonstrate that the prelimbic cortex was selectively involved in the cognitive and motivational processing of the learning task but not in the execution of reward-directed behaviors. In addition, the other recorded structures presented specific tendencies to be involved in these 2 types of brain activity in response to the presentation of GO or noGO stimuli. Spectral analyses, spectrograms, and coherence between the recorded brain areas indicate their specific involvement in GO vs. noGO tasks.

Beyond hippocampus: Thalamic and prefrontal contributions to an evolving memory

The hippocampus has long been at the center of memory research, and rightfully so. However, with emerging technological capabilities, we can increasingly appreciate memory as a more dynamic and brain-wide process. In this perspective, our goal is to begin developing models to understand the gradual evolution, reorganization, and stabilization of memories across the brain after their initial formation in the hippocampus. By synthesizing studies across the rodent and human literature, we suggest that as memory representations initially form in hippocampus, parallel traces emerge in frontal cortex that cue memory recall, and as they mature, with sustained support initially from limbic then diencephalic then cortical circuits, they become progressively independent of hippocampus and dependent on a mature cortical representation. A key feature of this model is that, as time progresses, memory representations are passed on to distinct circuits with progressively longer time constants, providing the opportunity to filter, forget, update, or reorganize memories in the process of committing to long-term storage.

Multi-ancestry phenome-wide association of complement component 4 variation with psychiatric and brain phenotypes in youth

BACKGROUND

Increased expression of the complement component 4A (C4A) gene is associated with a greater lifetime risk of schizophrenia. In the brain, C4A is involved in synaptic pruning; yet, it remains unclear the extent to which upregulation of C4A alters brain development or is associated with the risk for psychotic symptoms in childhood. Here, we perform a multi-ancestry phenome-wide association study in 7789 children aged 9-12 years to examine the relationship between genetically regulated expression (GREx) of C4A, childhood brain structure, cognition, and psychiatric symptoms.

RESULTS

While C4A GREx is not related to childhood psychotic experiences, cognition, or global measures of brain structure, it is associated with a localized reduction in regional surface area (SA) of the entorhinal cortex. Furthermore, we show that reduced entorhinal cortex SA at 9-10 years predicts a greater number and severity of psychosis-like events at 1-year and 2-year follow-up time points. We also demonstrate that the effects of C4A on the entorhinal cortex are independent of genome-wide polygenic risk for schizophrenia.

CONCLUSIONS

Our results suggest neurodevelopmental effects of C4A on childhood medial temporal lobe structure, which may serve as a biomarker for schizophrenia risk prior to symptom onset.

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.

Structural connectivity-based segmentation of the human entorhinal cortex

The medial (MEC) and lateral entorhinal cortex (LEC), widely studied in rodents, are well defined and characterized. In humans, however, the exact locations of their homologues remain uncertain. Previous functional magnetic resonance imaging (fMRI) studies have subdivided the human EC into posteromedial (pmEC) and anterolateral (alEC) parts, but uncertainty remains about the choice of imaging modality and seed regions, in particular in light of a substantial revision of the classical model of EC connectivity based on novel insights from rodent anatomy. Here, we used structural, not functional imaging, namely diffusion tensor imaging (DTI) and probabilistic tractography to segment the human EC based on differential connectivity to other brain regions known to project selectively to MEC or LEC. We defined MEC as more strongly connected with presubiculum and retrosplenial cortex (RSC), and LEC as more strongly connected with distal CA1 and proximal subiculum (dCA1pSub) and lateral orbitofrontal cortex (OFC). Although our DTI segmentation had a larger medial-lateral component than in the previous fMRI studies, our results show that the human MEC and LEC homologues have a border oriented both towards the posterior-anterior and medial-lateral axes, supporting the differentiation between pmEC and alEC.

Skipping ahead: A circuit for representing the past, present, and future

Envisioning the future is intuitively linked to our ability to remember the past. Within the memory system, substantial work has demonstrated the involvement of the prefrontal cortex and the hippocampus in representing the past and present. Recent data shows that both the prefrontal cortex and the hippocampus encode future trajectories, which are segregated in time by alternating cycles of the theta rhythm. Here, we discuss how information is temporally organized by these brain regions supported by the medial septum, nucleus reuniens, and parahippocampal regions. Finally, we highlight a brain circuit that we predict is essential for the temporal segregation of future scenarios.

Multi-Scale Extension in an Entorhinal-Hippocampal Model for Cognitive Map Building

Neuroscience research shows that, by relying on internal spatial representations provided by the hippocampus and entorhinal cortex, mammals are able to build topological maps of environments and navigate. Taking inspiration from mammals' spatial cognition mechanism, entorhinal-hippocampal cognitive systems have been proposed for robots to build cognitive maps. However, path integration and vision processing are time-consuming, and the existing model of grid cells is hard to achieve in terms of adaptive multi-scale extension for different environments, resulting in the lack of viability for real environments. In this work, an optimized dynamical model of grid cells is built for path integration in which recurrent weight connections between grid cells are parameterized in a more optimized way and the non-linearity of sigmoidal neural transfer function is utilized to enhance grid cell activity packets. Grid firing patterns with specific spatial scales can thus be accurately achieved for the multi-scale extension of grid cells. In addition, a hierarchical vision processing mechanism is proposed for speeding up loop closure detection. Experiment results on the robotic platform demonstrate that our proposed entorhinal-hippocampal model can successfully build cognitive maps, reflecting the robot's spatial experience and environmental topological structures.

A computational model for grid maps in neural populations

Grid cells in the entorhinal cortex, together with head direction, place, speed and border cells, are major contributors to the organization of spatial representations in the brain. In this work we introduce a novel theoretical and algorithmic framework able to explain the optimality of hexagonal grid-like response patterns. We show that this pattern is a result of minimal variance encoding of neurons together with maximal robustness to neurons' noise and minimal number of encoding neurons. The novelty lies in the formulation of the encoding problem considering neurons as an overcomplete basis (a frame) where the position information is encoded. Through the modern Frame Theory language, specifically that of tight and equiangular frames, we provide new insights about the optimality of hexagonal grid receptive fields. The proposed model is based on the well-accepted and tested hypothesis of Hebbian learning, providing a simplified cortical-based framework that does not require the presence of velocity-driven oscillations (oscillatory model) or translational symmetries in the synaptic connections (attractor model). We moreover demonstrate that the proposed encoding mechanism naturally explains axis alignment of neighbor grid cells and maps shifts, rotations and scaling of the stimuli onto the shape of grid cells' receptive fields, giving a straightforward explanation of the experimental evidence of grid cells remapping under transformations of environmental cues.

Synaptic plasticity/dysplasticity, process memory and item memory in rodent models of mental dysfunction

Activity-dependent changes in the effective connection strength of synapses are a fundamental feature of a nervous system. This so-called synaptic plasticity is thought to underlie storage of information in memory and has been hypothesized to be crucial for the effects of cognitive behavioral therapy. Synaptic plasticity stores information in a neural network, creating a trace of neural activity from past experience. The plasticity can also change the behavior of the network so the network can differentially transform/compute information in future activations. We discuss these two related but separable functions of synaptic plasticity; one we call "item memory" as it represents and stores items of information in memory, the other we call "process memory" as it encodes and stores functions such as computations to modify network information processing capabilities. We review evidence of item and process memory operations in behavior and evidence that experience modifies the brain's functional networks. We discuss neurodevelopmental rodent models relevant for understanding mental illness and compare two models in which one model, neonatal ventral hippocampal lesion (NVHL) has beneficial adult outcomes after being exposed to an adolescent cognitive experience that is potentially similar to cognitive behavioral therapy. The other model, gestational day 17 methylazoxymethanol acetate (GD17-MAM), does not benefit from the same adolescent cognitive experience. We propose that process memory is altered by early cognitive experience in NVHL rats but not in GD17-MAM rats, and discuss how dysplasticity factors may contribute to the differential adult outcomes after early cognitive experience in the NVHL and MAM models.

Position Estimation Based on Grid Cells and Self-Growing Self-Organizing Map

As the basis of animals' natal homing behavior, path integration can continuously provide current position information relative to the initial position. Some neurons in freely moving animals' brains can encode current positions and surrounding environments by special firing patterns. Research studies show that neurons such as grid cells (GCs) in the hippocampus of animals' brains are related to the path integration. They might encode the coordinate of the animal's current position in the same way as the residue number system (RNS) which is based on the Chinese remainder theorem (CRT). Hence, in order to provide vehicles a bionic position estimation method, we propose a model to decode the GCs' encoding information based on the improved traditional self-organizing map (SOM), and this model makes full use of GCs' firing characteristics. The details of the model are discussed in this paper. Besides, the model is realized by computer simulation, and its performance is analyzed under different conditions. Simulation results indicate that the proposed position estimation model is effective and stable.

How the Hippocampus Represents Memories: Making Sense of Memory Allocation Studies

In recent years there has been a wealth of studies investigating how memories are allocated in the hippocampus. Some of those studies showed that it is possible to manipulate the identity of neurons recruited to represent a given memory without affecting the memory's behavioral expression. Those findings raised questions about how the hippocampus represents memories, with some researchers arguing that hippocampal neurons do not represent fixed stimuli. Herein, an alternative hypothesis is argued. Neurons in high-order brain regions can be tuned to multiple dimensions, forming complex, abstract representations. It is argued that such complex receptive fields allow those neurons to show some flexibility in their responses while still representing relatively fixed sets of stimuli. Moreover, it is pointed out that changes induced by artificial manipulation of cell assemblies are not completely redundant-the observed behavioral redundancy does not imply cognitive redundancy, as different, but similar, memories may induce the same behavior.

Navigation Patterns and Scent Marking: Underappreciated Contributors to Hippocampal and Entorhinal Spatial Representations?

According to the currently prevailing theory, hippocampal formation constructs and maintains cognitive spatial maps. Most of the experimental evidence for this theory comes from the studies on navigation in laboratory rats and mice, typically male animals. While these animals exhibit a rich repertoire of behaviors associated with navigation, including locomotion, head movements, whisking, sniffing, raring and scent marking, the contribution of these behavioral patterns to the hippocampal spatially-selective activity has not been sufficiently studied. Instead, many publications have considered animal position in space as the major variable that affects the firing of hippocampal place cells and entorhinal grid cells. Here we argue that future work should focus on a more detailed examination of different behaviors exhibited during navigation to better understand the mechanism of spatial tuning in hippocampal neurons. As an inquiry in this direction, we have analyzed data from two datasets, shared online, containing recordings from rats navigating in square and round arenas. Our analyses revealed patchy navigation patterns, evident from the spatial maps of animal position, velocity and acceleration. Moreover, grid cells available in the datasets exhibited similar periodicity as the navigation parameters. These findings indicate that activity of grid cells could affect navigation parameters and/or vice versa. Additionally, we speculate that scent marks left by navigating animals could contribute to neuronal responses while rats and mice sniff their environment; the act of sniffing could modulate neuronal discharges even in virtual visual environments. Accordingly, we propose that future experiments should contain additional controls for navigation patterns, whisking, sniffing and maps composed of scent marks.

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.

MicroRNAs contribute to postnatal development of laminar differences and neuronal subtypes in the rat medial entorhinal cortex

The medial entorhinal cortex (MEC) is important in spatial navigation and memory formation and its layers have distinct neuronal subtypes, connectivity, spatial properties, and disease susceptibility. As little is known about the molecular basis for the development of these laminar differences, we analyzed microRNA (miRNA) and messenger RNA (mRNA) expression differences between rat MEC layer II and layers III–VI during postnatal development. We identified layer and age-specific regulation of gene expression by miRNAs, which included processes related to neuron specialization and locomotor behavior. Further analyses by retrograde labeling and expression profiling of layer II stellate neurons and in situ hybridization revealed that the miRNA most up-regulated in layer II, miR-143, was enriched in stellate neurons, whereas the miRNA most up-regulated in deep layers, miR-219-5p, was expressed in ependymal cells, oligodendrocytes and glia. Bioinformatics analyses of predicted mRNA targets with negatively correlated expression patterns to miR-143 found that miR-143 likely regulates the Lmo4 gene, which is known to influence hippocampal-based spatial learning.

Functional states of rat cortical circuits during the unpredictable availability of a reward-related cue

Proper performance of acquired abilities can be disturbed by the unexpected occurrence of external changes. Rats trained with an operant conditioning task (to press a lever in order to obtain a food pellet) using a fixed-ratio (1:1) schedule were subsequently placed in a Skinner box in which the lever could be removed randomly. Field postsynaptic potentials (fPSPs) were chronically evoked in perforant pathway-hippocampal CA1 (PP-CA1), CA1-subiculum (CA1-SUB), CA1-medial prefrontal cortex (CA1-mPFC), mPFC-nucleus accumbens (mPFC-NAc), and mPFC-basolateral amygdala (mPFC-BLA) synapses during lever IN and lever OUT situations. While lever presses were accompanied by a significant increase in fPSP slopes at the five synapses, the unpredictable absence of the lever were accompanied by decreased fPSP slopes in all, except PP-CA1 synapses. Spectral analysis of local field potentials (LFPs) recorded when the animal approached the corresponding area in the lever OUT situation presented lower spectral powers than during lever IN occasions for all recording sites, apart from CA1. Thus, the unpredictable availability of a reward-related cue modified the activity of cortical and subcortical areas related with the acquisition of operant learning tasks, suggesting an immediate functional reorganization of these neural circuits to address the changed situation and to modify ongoing behaviors accordingly.

Functional Architecture of the Rat Parasubiculum

The parasubiculum is a major input structure of layer 2 of medial entorhinal cortex, where most grid cells are found. Here we investigated parasubicular circuits of the rat by anatomical analysis combined with juxtacellular recording/labeling and tetrode recordings during spatial exploration. In tangential sections, the parasubiculum appears as a linear structure flanking the medial entorhinal cortex mediodorsally. With a length of ∼5.2 mm and a width of only ∼0.3 mm (approximately one dendritic tree diameter), the parasubiculum is both one of the longest and narrowest cortical structures. Parasubicular neurons span the height of cortical layers 2 and 3, and we observed no obvious association of deep layers to this structure. The "superficial parasubiculum" (layers 2 and 1) divides into ∼15 patches, whereas deeper parasubicular sections (layer 3) form a continuous band of neurons. Anterograde tracing experiments show that parasubicular neurons extend long "circumcurrent" axons establishing a "global" internal connectivity. The parasubiculum is a prime target of GABAergic and cholinergic medial septal inputs. Other input structures include the subiculum, presubiculum, and anterior thalamus. Functional analysis of identified and unidentified parasubicular neurons shows strong theta rhythmicity of spiking, a large fraction of head-direction selectivity (50%, 34 of 68), and spatial responses (grid, border and irregular spatial cells, 57%, 39 of 68). Parasubicular output preferentially targets patches of calbindin-positive pyramidal neurons in layer 2 of medial entorhinal cortex, which might be relevant for grid cell function. These findings suggest the parasubiculum might shape entorhinal theta rhythmicity and the (dorsoventral) integration of information across grid scales.

SIGNIFICANCE STATEMENT

Grid cells in medial entorhinal cortex (MEC) are crucial components of an internal navigation system of the mammalian brain. The parasubiculum is a major input structure of layer 2 of MEC, where most grid cells are found. Here we provide a functional and anatomical characterization of the parasubiculum and show that parasubicular neurons display unique features (i.e., strong theta rhythmicity of firing, prominent head-direction selectivity, and output selectively targeted to layer 2 pyramidal cell patches of MEC). These features could contribute to shaping the temporal and spatial code of downstream grid cells in entorhinal cortex.

Distributed Representation of Curvilinear Self-Motion in the Macaque Parietal Cortex

Information about translations and rotations of the body is critical for complex self-motion perception during spatial navigation. However, little is known about the nature and function of their convergence in the cortex. We measured neural activity in multiple areas in the macaque parietal cortex in response to three different types of body motion applied through a motion platform: translation, rotation, and combined stimuli, i.e., curvilinear motion. We found a continuous representation of motion types in each area. In contrast to single-modality cells preferring either translation-only or rotation-only stimuli, convergent cells tend to be optimally tuned to curvilinear motion. A weighted summation model captured the data well, suggesting that translation and rotation signals are integrated subadditively in the cortex. Interestingly, variation in the activity of convergent cells parallels behavioral outputs reported in human psychophysical experiments. We conclude that representation of curvilinear self-motion perception is widely distributed in the primate sensory cortex.

Complementary Functional Organization of Neuronal Activity Patterns in the Perirhinal, Lateral Entorhinal, and Medial Entorhinal Cortices

It is commonly conceived that the cortical areas of the hippocampal region are functionally divided into the perirhinal cortex (PRC) and the lateral entorhinal cortex (LEC), which selectively process object information; and the medial entorhinal cortex (MEC), which selectively processes spatial information. Contrary to this notion, in rats performing a task that demands both object and spatial information processing, single neurons in PRC, LEC, and MEC, including those in both superficial and deep cortical areas and in grid, border, and head direction cells of MEC, have a highly similar range of selectivity to object and spatial dimensions of the task. By contrast, representational similarity analysis of population activity reveals a key distinction in the organization of information in these areas, such that PRC and LEC populations prioritize object over location information, whereas MEC populations prioritize location over object information. These findings bring to the hippocampal system a growing emphasis on population analyses as a powerful tool for characterizing neural representations supporting cognition and memory.

SIGNIFICANCE STATEMENT

Contrary to the common view that brain regions in the "what" and "where" streams distinctly process object and spatial cues, respectively, we found that both streams encode both object and spatial information but distinctly organize memories for objects and space. Specifically, perirhinal cortex and lateral entorhinal cortex represent objects and, within the object-specific representations, the locations where they occur. Conversely, medial entorhinal cortex represents relevant locations and, within those spatial representations, the objects that occupy them. Furthermore, these findings reach beyond simple notions of perirhinal cortex and lateral entorhinal cortex neurons as object detectors and MEC neurons as position detectors, and point to a more complex organization of memory representations within the medial temporal lobe system.

Cortical Correlates of Low-Level Perception: From Neural Circuits to Percepts

Low-level perception results from neural-based computations, which build a multimodal skeleton of unconscious or self-generated inferences on our environment. This review identifies bottleneck issues concerning the role of early primary sensory cortical areas, mostly in rodent and higher mammals (cats and non-human primates), where perception substrates can be searched at multiple scales of neural integration. We discuss the limitation of purely bottom-up approaches for providing realistic models of early sensory processing and the need for identification of fast adaptive processes, operating within the time of a percept. Future progresses will depend on the careful use of comparative neuroscience (guiding the choices of experimental models and species adapted to the questions under study), on the definition of agreed-upon benchmarks for sensory stimulation, on the simultaneous acquisition of neural data at multiple spatio-temporal scales, and on the in vivo identification of key generic integration and plasticity algorithms validated experimentally and in simulations.

Anatomical Organization and Spatiotemporal Firing Patterns of Layer 3 Neurons in the Rat Medial Entorhinal Cortex

Layer 3 of the medial entorhinal cortex is a major gateway from the neocortex to the hippocampus. Here we addressed structure-function relationships in medial entorhinal cortex layer 3 by combining anatomical analysis with juxtacellular identification of single neurons in freely behaving rats. Anatomically, layer 3 appears as a relatively homogeneous cell sheet. Dual-retrograde neuronal tracing experiments indicate a large overlap between layer 3 pyramidal populations, which project to ipsilateral hippocampus, and the contralateral medial entorhinal cortex. These cells were intermingled within layer 3, and had similar morphological and intrinsic electrophysiological properties. Dendritic trees of layer 3 neurons largely avoided the calbindin-positive patches in layer 2. Identification of layer 3 neurons during spatial exploration (n = 17) and extracellular recordings (n = 52) pointed to homogeneous spatial discharge patterns. Layer 3 neurons showed only weak spiking theta rhythmicity and sparse head-direction selectivity. A majority of cells (50 of 69) showed no significant spatial modulation. All of the ∼28% of neurons that carried significant amounts of spatial information (19 of 69) discharged in irregular spatial patterns. Thus, layer 3 spatiotemporal firing properties are remarkably different from those of layer 2, where theta rhythmicity is prominent and spatially modulated cells often discharge in grid or border patterns. Significance statement: Neurons within the superficial layers of the medial entorhinal cortex (MEC) often discharge in border, head-direction, and theta-modulated grid patterns. It is still largely unknown how defined discharge patterns relate to cellular diversity in the superficial layers of the MEC. In the present study, we addressed this issue by combining anatomical analysis with juxtacellular identification of single layer 3 neurons in freely behaving rats. We provide evidence that the anatomical organization and spatiotemporal firing properties of layer 3 neurons are remarkably different from those in layer 2. Specifically, most layer 3 neurons discharged in spatially irregular firing patterns, with weak theta-modulation and head-directional selectivity. This work thus poses constraints on the spatiotemporal patterns reaching downstream targets, like the hippocampus.

Noise promotes independent control of gamma oscillations and grid firing within recurrent attractor networks

Neural computations underlying cognitive functions require calibration of the strength of excitatory and inhibitory synaptic connections and are associated with modulation of gamma frequency oscillations in network activity. However, principles relating gamma oscillations, synaptic strength and circuit computations are unclear. We address this in attractor network models that account for grid firing and theta-nested gamma oscillations in the medial entorhinal cortex. We show that moderate intrinsic noise massively increases the range of synaptic strengths supporting gamma oscillations and grid computation. With moderate noise, variation in excitatory or inhibitory synaptic strength tunes the amplitude and frequency of gamma activity without disrupting grid firing. This beneficial role for noise results from disruption of epileptic-like network states. Thus, moderate noise promotes independent control of multiplexed firing rate- and gamma-based computational mechanisms. Our results have implications for tuning of normal circuit function and for disorders associated with changes in gamma oscillations and synaptic strength.

DOI:http://dx.doi.org/10.7554/eLife.06444.001

When electrodes are placed on the scalp, or lowered into the brain itself, rhythmic waves of electrical activity are seen that reflect the coordinated firing of large numbers of neurons. The pattern of the waves varies between different brain regions, and according to what the animal or person is doing. During sleep and quiet wakefulness, slower brain waves predominate, whereas faster waves called gamma oscillations emerge during cognition—the act of processing knowledge.

Gamma waves can be readily detected in a region of the brain called the medial entorhinal cortex (MEC). This brain region is also known for its role in forming the spatial memories that allow an individual to remember how to navigate around an area they have previously visited. Individual MEC cells increase their firing rates whenever an individual is at specific locations. When these locations are plotted in two dimensions, they form a hexagonal grid: this ‘grid cell map’ enables the animal to keep track of its position as it navigates through its environment.

To determine how MEC neurons can simultaneously encode spatial locations and generate the gamma waves implicated in cognition, Solanka et al. have used supercomputing to simulate the activity of more than 1.5 million connections between MEC cells. Changing the strength of these connections had different effects on the ability of the MEC to produce gamma waves or spatial maps. However, adjusting the model to include random fluctuations in neuronal firing, or ‘noise’, was beneficial for both types of output. This is partly because noise prevented neuronal firing from becoming excessively synchronized, which would otherwise have caused seizures.

Although noise is generally regarded as disruptive, the results of Solanka et al. suggest that it helps the MEC to perform its two distinct roles. Specifically, the presence of noise enables relatively small changes in the strength of the connections between neurons to alter gamma waves—and thus affect cognition—without disrupting the neurons' ability to encode spatial locations. Given that noise reduces the likelihood of seizures, the results also raise the possibility that introducing noise into the brain in a controlled way could have therapeutic benefits for individuals with epilepsy.

DOI:http://dx.doi.org/10.7554/eLife.06444.002

Functional topography of the human entorhinal cortex

Despite extensive research on the role of the rodent medial and lateral entorhinal cortex (MEC/LEC) in spatial navigation, memory and related disease, their human homologues remain elusive. Here, we combine high-field functional magnetic resonance imaging at 7 T with novel data-driven and model-based analyses to identify corresponding subregions in humans based on the well-known global connectivity fingerprints in rodents and sensitivity to spatial and non-spatial information. We provide evidence for a functional division primarily along the anteroposterior axis. Localising the human homologue of the rodent MEC and LEC has important implications for translating studies on the hippocampo-entorhinal memory system from rodents to humans.

DOI:http://dx.doi.org/10.7554/eLife.06738.001

In the early 1950s, an American named Henry Molaison underwent an experimental type of brain surgery to treat his severe epilepsy. The surgeon removed a region of the brain known as the temporal lobe from both sides of his brain. After the surgery, Molaison's epilepsy was greatly improved, but he was also left with a profound amnesia, unable to form new memories of recent events.

Subsequent experiments, including many with Molaison himself as a subject, have attempted to identify the roles of the various structures within the temporal lobes. The hippocampus—which is involved in memory and spatial navigation—has received the most attention, but in recent years a region called the entorhinal cortex has also come to the fore. Known as the gateway to the hippocampus, the entorhinal cortex relays sensory information from the outer cortex of the brain to the hippocampus.

In rats and mice the entorhinal cortex can be divided into two subregions that have distinct connections to other parts of the temporal lobe and to the rest of the brain. These are the medial entorhinal cortex, which is the subregion nearest the centre of the brain, and the lateral entorhinal cortex, which is to the left or right of the centre.

For many years researchers had assumed that human entorhinal subregions were located simply to the center or to the sides of the brain. However, it was difficult to check this as the entorhinal cortex measures less than 1 cm across, which placed it beyond the reach of most brain-imaging techniques. Now, two independent groups of researchers have used a technique called functional magnetic resonance imaging to show a different picture. The fMRI data—which were collected in a magnetic field of 7 Tesla, rather than the 1.5 Tesla used in previous experiments—reveal that the entorhinal cortex is predominantly divided from front-to-back in humans.

One of the groups—Navarro Schröder, Haak et al.—used three different sets of functional MRI data to show that the human entorhinal cortex has anterior-lateral and posterior-medial subregions. In one of these experiments, functional MRI was used to measure activity across the whole brain as subjects performed a virtual reality task: this task included some components that involved spatial navigation and other components that did not. The other group—Maass, Berron et al.—used the imaging data to show that the pattern of connections between the anterior-lateral subregion and the hippocampus was different to that between the posterior-medial subregion and the hippocampus.

The discovery of these networks in the temporal lobe in humans will help to bridge the gap between studies of memory in rodents and in humans. Given that the lateral entorhinal cortex is one of the first regions to be affected in Alzheimer's disease, identifying the specific properties and roles of these networks could also provide insights into disease mechanisms.

DOI:http://dx.doi.org/10.7554/eLife.06738.002

Functional subregions of the human entorhinal cortex

The entorhinal cortex (EC) is the primary site of interactions between the neocortex and hippocampus. Studies in rodents and nonhuman primates suggest that EC can be divided into subregions that connect differentially with perirhinal cortex (PRC) vs parahippocampal cortex (PHC) and with hippocampal subfields along the proximo-distal axis. Here, we used high-resolution functional magnetic resonance imaging at 7 Tesla to identify functional subdivisions of the human EC. In two independent datasets, PRC showed preferential intrinsic functional connectivity with anterior-lateral EC and PHC with posterior-medial EC. These EC subregions, in turn, exhibited differential connectivity with proximal and distal subiculum. In contrast, connectivity of PRC and PHC with subiculum followed not only a proximal-distal but also an anterior-posterior gradient. Our data provide the first evidence that the human EC can be divided into functional subdivisions whose functional connectivity closely parallels the known anatomical connectivity patterns of the rodent and nonhuman primate EC.

The subiculum: the heart of the extended hippocampal system

While descriptions of the subiculum often emphasize its role as a recipient of hippocampal inputs, the area also has particular importance as a source of hippocampal projections. The extrinsic projections from the subiculum not only parallel those from hippocampal fields CA1-4 but also terminate in sites that do not receive direct inputs from the rest of the hippocampus. Both electrophysiological and lesion studies reveal how, despite its very dense CA1 inputs, the subiculum has functional properties seemingly independent from the rest of the hippocampus. In understanding the subiculum, it is necessary to appreciate that its connections are topographically organized along all three planes (longitudinal, transverse, and depth). These topographies may enable the subiculum to separate multiple information types and, hence, support multiple functions. The particular significance of the subiculum for learning and memory is underlined by its importance as a source of hippocampal projections to nuclei in the medial diencephalon, which are themselves vital for human memory and rodent spatial learning. Of particular note are its reciprocal connections with the anterior thalamic nuclei, which are not shared by the rest of the hippocampus (CA1-4). These thalamosubiculum connections may be of especial significance for resolving memory problems that suffer high interference and require the flexible use of stimulus representations.

Repeating spatial activations in human entorhinal cortex

The ability to remember and navigate spatial environments is critical for everyday life. A primary mechanism by which the brain represents space is through hippocampal place cells, which indicate when an animal is at a particular location. An important issue is understanding how the hippocampal place-cell network represents specific properties of the environment, such as signifying that a particular position is near a doorway or that another position is near the end of a corridor. The entorhinal cortex (EC), as the main input to the hippocampus, may play a key role in coding these properties because it contains neurons that activate at multiple related positions per environment. We examined the diversity of spatial coding across the human medial temporal lobe by recording neuronal activity during virtual navigation of an environment containing four similar paths. Neurosurgical patients performed this task as we recorded from implanted microelectrodes, allowing us to compare the human neuronal representation of space with that of animals. EC neurons activated in a repeating manner across the environment, with individual cells spiking at the same relative location across multiple paths. This finding indicates that EC cells represent non-specific information about location relative to an environment's geometry, unlike hippocampal place cells, which activate at particular random locations. Given that spatial navigation is considered to be a model of how the brain supports non-spatial episodic memory, these findings suggest that EC neuronal activity is used by the hippocampus to represent the properties of different memory episodes.

Laminar and dorsoventral molecular organization of the medial entorhinal cortex revealed by large-scale anatomical analysis of gene expression

Neural circuits in the medial entorhinal cortex (MEC) encode an animal's position and orientation in space. Within the MEC spatial representations, including grid and directional firing fields, have a laminar and dorsoventral organization that corresponds to a similar topography of neuronal connectivity and cellular properties. Yet, in part due to the challenges of integrating anatomical data at the resolution of cortical layers and borders, we know little about the molecular components underlying this organization. To address this we develop a new computational pipeline for high-throughput analysis and comparison of in situ hybridization (ISH) images at laminar resolution. We apply this pipeline to ISH data for over 16,000 genes in the Allen Brain Atlas and validate our analysis with RNA sequencing of MEC tissue from adult mice. We find that differential gene expression delineates the borders of the MEC with neighboring brain structures and reveals its laminar and dorsoventral organization. We propose a new molecular basis for distinguishing the deep layers of the MEC and show that their similarity to corresponding layers of neocortex is greater than that of superficial layers. Our analysis identifies ion channel-, cell adhesion- and synapse-related genes as candidates for functional differentiation of MEC layers and for encoding of spatial information at different scales along the dorsoventral axis of the MEC. We also reveal laminar organization of genes related to disease pathology and suggest that a high metabolic demand predisposes layer II to neurodegenerative pathology. In principle, our computational pipeline can be applied to high-throughput analysis of many forms of neuroanatomical data. Our results support the hypothesis that differences in gene expression contribute to functional specialization of superficial layers of the MEC and dorsoventral organization of the scale of spatial representations.

Pyramidal and stellate cell specificity of grid and border representations in layer 2 of medial entorhinal cortex

In medial entorhinal cortex, layer 2 principal cells divide into pyramidal neurons (mostly calbindin positive) and dentate gyrus-projecting stellate cells (mostly calbindin negative). We juxtacellularly labeled layer 2 neurons in freely moving animals, but small sample size prevented establishing unequivocal structure-function relationships. We show, however, that spike locking to theta oscillations allows assigning unidentified extracellular recordings to pyramidal and stellate cells with ∼83% and ∼89% specificity, respectively. In pooled anatomically identified and theta-locking-assigned recordings, nonspatial discharges dominated, and weakly hexagonal spatial discharges and head-direction selectivity were observed in both cell types. Clear grid discharges were rare and mostly classified as pyramids (19%, 19/99 putative pyramids versus 3%, 3/94 putative stellates). Most border cells were classified as stellate (11%, 10/94 putative stellates versus 1%, 1/99 putative pyramids). Our data suggest weakly theta-locked stellate border cells provide spatial input to dentate gyrus, whereas strongly theta-locked grid discharges occur mainly in hexagonally arranged pyramidal cell patches and do not feed into dentate gyrus.

•

Stellates and pyramids can be putatively assigned by spike locking to theta

•

Grid cells in entorhinal layer 2 largely correspond to putative pyramidal cells

•

Border responses in layer 2 largely correspond to putative stellate cells

Juxtacellular recording and morphological identification of single neurons in freely moving rats

It is well established that neural circuits consist of a great diversity of cell types, but very little is known about how neuronal diversity contributes to cognition and behavior. One approach to addressing this problem is to directly link cellular diversity to neuronal activity recorded in vivo in behaving animals. Here we describe the technical procedures for obtaining juxtacellular recordings from single neurons in trained rats engaged in exploratory behavior. The recorded neurons can be labeled to allow subsequent anatomical identification. In its current format, the protocol can be used for resolving the cellular identity of spatially modulated neurons (i.e., head-direction cells and grid cells), which form the basis of the animal's internal representation of space, but this approach can easily be extended to other unrestrained behaviors. The procedures described here, from the beginning of animal training to the histological processing of brain sections, can be completed in ≈ 3-4 weeks.

Grid cells and cortical representation

One of the grand challenges in neuroscience is to comprehend neural computation in the association cortices, the parts of the cortex that have shown the largest expansion and differentiation during mammalian evolution and that are thought to contribute profoundly to the emergence of advanced cognition in humans. In this Review, we use grid cells in the medial entorhinal cortex as a gateway to understand network computation at a stage of cortical processing in which firing patterns are shaped not primarily by incoming sensory signals but to a large extent by the intrinsic properties of the local circuit.

Neurosharing: large-scale data sets (spike, LFP) recorded from the hippocampal-entorhinal system in behaving rats

Using silicon-based recording electrodes, we recorded neuronal activity of the dorsal hippocampus and dorsomedial entorhinal cortex from behaving rats. The entorhinal neurons were classified as principal neurons and interneurons based on monosynaptic interactions and wave-shapes. The hippocampal neurons were classified as principal neurons and interneurons based on monosynaptic interactions, wave-shapes and burstiness. The data set contains recordings from 7,736 neurons (6,100 classified as principal neurons, 1,132 as interneurons, and 504 cells that did not clearly fit into either category) obtained during 442 recording sessions from 11 rats (a total of 204.5 hours) while they were engaged in one of eight different behaviours/tasks. Both original and processed data (time stamp of spikes, spike waveforms, result of spike sorting and local field potential) are included, along with metadata of behavioural markers. Community-driven data sharing may offer cross-validation of findings, refinement of interpretations and facilitate discoveries.