Entorhinal cortex grid cells can map to hippocampal place cells by competitive learning

A Model of Generating Visual Place Cells Based on Environment Perception and Similar Measure

It is an important content to generate visual place cells (VPCs) in the field of bioinspired navigation. By analyzing the firing characteristic of biological place cells and the existing methods for generating VPCs, a model of generating visual place cells based on environment perception and similar measure is abstracted in this paper. VPCs' generation process is divided into three phases, including environment perception, similar measure, and recruiting of a new place cell. According to this process, a specific method for generating VPCs is presented. External reference landmarks are obtained based on local invariant characteristics of image and a similar measure function is designed based on Euclidean distance and Gaussian function. Simulation validates the proposed method is available. The firing characteristic of the generated VPCs is similar to that of biological place cells, and VPCs' firing fields can be adjusted flexibly by changing the adjustment factor of firing field (AFFF) and firing rate's threshold (FRT).

A Topological Model of the Hippocampal Cell Assembly Network

It is widely accepted that the hippocampal place cells' spiking activity produces a cognitive map of space. However, many details of this representation's physiological mechanism remain unknown. For example, it is believed that the place cells exhibiting frequent coactivity form functionally interconnected groups-place cell assemblies-that drive readout neurons in the downstream networks. However, the sheer number of coactive combinations is extremely large, which implies that only a small fraction of them actually gives rise to cell assemblies. The physiological processes responsible for selecting the winning combinations are highly complex and are usually modeled via detailed synaptic and structural plasticity mechanisms. Here we propose an alternative approach that allows modeling the cell assembly network directly, based on a small number of phenomenological selection rules. We then demonstrate that the selected population of place cell assemblies correctly encodes the topology of the environment in biologically plausible time, and may serve as a schematic model of the hippocampal network.

Ten Years of Grid Cells

The medial entorhinal cortex (MEC) creates a neural representation of space through a set of functionally dedicated cell types: grid cells, border cells, head direction cells, and speed cells. Grid cells, the most abundant functional cell type in the MEC, have hexagonally arranged firing fields that tile the surface of the environment. These cells were discovered only in 2005, but after 10 years of investigation, we are beginning to understand how they are organized in the MEC network, how their periodic firing fields might be generated, how they are shaped by properties of the environment, and how they interact with the rest of the MEC network. The aim of this review is to summarize what we know about grid cells and point out where our knowledge is still incomplete.

Neuromorphic VLSI realization of the hippocampal formation

The medial entorhinal cortex grid cells, aided by the subicular head direction cells, are thought to provide a matrix which is utilized by the hippocampal place cells for calculation of position of an animal during spatial navigation. The place cells are thought to function as an internal GPS for the brain and provide a spatiotemporal stamp on episodic memories. Several computational neuroscience models have been proposed to explain the place specific firing patterns of the cells of the hippocampal formation - including the GRIDSmap model for grid cells and Bayesian integration for place cells. In this work, we present design and measurement results from a first ever system of silicon circuits which successfully realize the function of the hippocampal formation of brain based on these models.

Hippocampal Remapping after Partial Inactivation of the Medial Entorhinal Cortex

Hippocampal place cells undergo remapping when the environment is changed. The mechanism of hippocampal remapping remains elusive but spatially modulated cells in the medial entorhinal cortex (MEC) have been identified as a possible contributor. Using pharmacogenetic and optogenetic approaches, we tested the role of MEC cells by examining in mice whether partial inactivation in MEC shifts hippocampal activity to a different subset of place cells with different receptive fields. The pharmacologically selective designer Gi-protein-coupled muscarinic receptor hM4D or the light-responsive microbial proton pump archaerhodopsin (ArchT) was expressed in MEC, and place cells were recorded after application of the inert ligand clozapine-N-oxide (CNO) or light at appropriate wavelengths. CNO or light caused partial inactivation of the MEC. The inactivation was followed by substantial remapping in the hippocampus, without disruption of the spatial firing properties of individual neurons. The results point to MEC input as an element of the mechanism for remapping in place cells.

Using Grid Cells for Navigation

Mammals are able to navigate to hidden goal locations by direct routes that may traverse previously unvisited terrain. Empirical evidence suggests that this “vector navigation” relies on an internal representation of space provided by the hippocampal formation. The periodic spatial firing patterns of grid cells in the hippocampal formation offer a compact combinatorial code for location within large-scale space. Here, we consider the computational problem of how to determine the vector between start and goal locations encoded by the firing of grid cells when this vector may be much longer than the largest grid scale. First, we present an algorithmic solution to the problem, inspired by the Fourier shift theorem. Second, we describe several potential neural network implementations of this solution that combine efficiency of search and biological plausibility. Finally, we discuss the empirical predictions of these implementations and their relationship to the anatomy and electrophysiology of the hippocampal formation.

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Grid cells (GCs) are believed to provide a path integration input to place cells

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However, GCs also provide a powerful context-independent metric for large-scale space

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Hence, we show how GCs can be used for vector navigation between arbitrary locations

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We simulate various neural implementations and make testable experimental predictions

Transient optogenetic inactivation of the medial entorhinal cortex biases the active population of hippocampal neurons

The mechanisms that enable the hippocampal network to express the appropriate spatial representation for a particular circumstance are not well understood. Previous studies suggest that the medial entorhinal cortex (MEC) may have a role in reproducibly selecting the hippocampal representation of an environment. To examine how ongoing MEC activity is continually integrated by the hippocampus, we performed transient unilateral optogenetic inactivations of the MEC while simultaneously recording place cell activity in CA1. Inactivation of the MEC caused a partial remapping in the CA1 population without diminishing the degree of spatial tuning across the active cell assembly. These changes remained stable irrespective of intermittent disruption of MEC input, indicating that while MEC input is integrated over long time scales to bias the active population, there are mechanisms for stabilizing the population of active neurons independent of the MEC. We find that MEC inputs to the hippocampus shape its ongoing activity by biasing the participation of the neurons in the active network, thereby influencing how the hippocampus selectively represents information.

Inheritance of Hippocampal Place Fields Through Hebbian Learning: Effects of Theta Modulation and Phase Precession on Structure Formation

A place cell is a neuron that fires whenever the animal traverses a particular location of the environment—the place field of the cell. Place cells are found in two regions of the rodent hippocampus: CA3 and CA1. Motivated by the anatomical connectivity between these two regions and by the evidence for synaptic plasticity at these connections, we study how a place field in CA1 can be inherited from an upstream region such as CA3 through a Hebbian learning rule, in particular, through spike-timing-dependent plasticity (STDP). To this end, we model a population of CA3 place cells projecting to a single CA1 cell, and we assume that the CA1 input synapses are plastic according to STDP. With both numerical and analytical methods, we show that in the case of overlapping CA3 input place fields, the STDP learning rule leads to the formation of a place field in CA1. We then investigate the roles of the hippocampal theta modulation and phase precession on the inheritance process. We find that theta modulation favors the inheritance and leads to faster place field formation whereas phase precession changes the drift of CA1 place fields over time.

Pattern separation, completion, and categorisation in the hippocampus and neocortex

The mechanisms for pattern completion and pattern separation are described in the context of a theory of hippocampal function in which the hippocampal CA3 system operates as a single attractor or autoassociation network to enable rapid, one-trial, associations between any spatial location (place in rodents, or spatial view in primates) and an object or reward, and to provide for completion of the whole memory during recall from any part. The factors important in the pattern completion in CA3 and also a large number of independent memories stored in CA3 include: a sparse distributed representation, representations that are independent due to the randomizing effect of the mossy fibres, heterosynaptic long-term depression as well as long-term potentiation in the recurrent collateral synapses, and diluted connectivity to minimize the number of multiple synapses between any pair of CA3 neurons which otherwise distort the basins of attraction. Recall of information from CA3 is implemented by the entorhinal cortex perforant path synapses to CA3 cells, which in acting as a pattern associator allow some pattern generalization. Pattern separation is performed in the dentate granule cells using competitive learning to convert grid-like entorhinal cortex firing to place-like fields, and in the dentate to CA3 connections that have diluted connectivity. Recall to the neocortex is achieved by a reverse hierarchical series of pattern association networks implemented by the hippocampo-cortical backprojections, each one of which performs some pattern generalization, to retrieve a complete pattern of cortical firing in higher-order cortical areas. New results on competitive networks show which factors contribute to their ability to perform pattern separation, pattern clustering, and pattern categorisation, and how these apply in different hippocampal and neocortical systems.

Conjunctive input processing drives feature selectivity in hippocampal CA1 neurons

Feature-selective firing allows networks to produce representations of the external and internal environments. Despite its importance, the mechanisms generating neuronal feature selectivity are incompletely understood. In many cortical microcircuits the integration of two functionally distinct inputs occurs nonlinearly through generation of active dendritic signals that drive burst firing and robust plasticity. To examine the role of this processing in feature selectivity, we recorded CA1 pyramidal neuron membrane potential and local field potential in mice running on a linear treadmill. We found that dendritic plateau potentials were produced by an interaction between properly timed input from entorhinal cortex and hippocampal CA3. These conjunctive signals positively modulated the firing of previously established place fields and rapidly induced new place field formation to produce feature selectivity in CA1 that is a function of both entorhinal cortex and CA3 input. Such selectivity could allow mixed network level representations that support context-dependent spatial maps.

Diluted connectivity in pattern association networks facilitates the recall of information from the hippocampus to the neocortex

The recall of information stored in the hippocampus involves a series of corticocortical backprojections via the entorhinal cortex, parahippocampal gyrus, and one or more neocortical stages. Each stage is considered to be a pattern association network, with the retrieval cue at each stage the firing of neurons in the previous stage. The leading factor that determines the capacity of this multistage pattern association backprojection pathway is the number of connections onto any one neuron, which provides a quantitative basis for why there are as many backprojections between adjacent stages in the hierarchy as forward projections. The issue arises of why this multistage backprojection system uses diluted connectivity. One reason is that a multistage backprojection system with expansion of neuron numbers at each stage enables the hippocampus to address during recall the very large numbers of neocortical neurons, which would otherwise require hippocampal neurons to make very large numbers of synapses if they were directly onto neocortical neurons. The second reason is that as shown here, diluted connectivity in the backprojection pathways reduces the probability of more than one connection onto a receiving neuron in the backprojecting pathways, which otherwise reduces the capacity of the system, that is the number of memories that can be recalled from the hippocampus to the neocortex. For similar reasons, diluted connectivity is advantageous in pattern association networks in other brain systems such as the orbitofrontal cortex and amygdala; for related reasons, in autoassociation networks in, for example, the hippocampal CA3 and the neocortex; and for the different reason that diluted connectivity facilitates the operation of competitive networks in forward-connected cortical systems.

Memory storage fidelity in the hippocampal circuit: the role of subregions and input statistics

In the last decades a standard model regarding the function of the hippocampus in memory formation has been established and tested computationally. It has been argued that the CA3 region works as an auto-associative memory and that its recurrent fibers are the actual storing place of the memories. Furthermore, to work properly CA3 requires memory patterns that are mutually uncorrelated. It has been suggested that the dentate gyrus orthogonalizes the patterns before storage, a process known as pattern separation. In this study we review the model when random input patterns are presented for storage and investigate whether it is capable of storing patterns of more realistic entorhinal grid cell input. Surprisingly, we find that an auto-associative CA3 net is redundant for random inputs up to moderate noise levels and is only beneficial at high noise levels. When grid cell input is presented, auto-association is even harmful for memory performance at all levels. Furthermore, we find that Hebbian learning in the dentate gyrus does not support its function as a pattern separator. These findings challenge the standard framework and support an alternative view where the simpler EC-CA1-EC network is sufficient for memory storage.

Simultaneous silence organizes structured higher-order interactions in neural populations

Activity patterns of neural population are constrained by underlying biological mechanisms. These patterns are characterized not only by individual activity rates and pairwise correlations but also by statistical dependencies among groups of neurons larger than two, known as higher-order interactions (HOIs). While HOIs are ubiquitous in neural activity, primary characteristics of HOIs remain unknown. Here, we report that simultaneous silence (SS) of neurons concisely summarizes neural HOIs. Spontaneously active neurons in cultured hippocampal slices express SS that is more frequent than predicted by their individual activity rates and pairwise correlations. The SS explains structured HOIs seen in the data, namely, alternating signs at successive interaction orders. Inhibitory neurons are necessary to maintain significant SS. The structured HOIs predicted by SS were observed in a simple neural population model characterized by spiking nonlinearity and correlated input. These results suggest that SS is a ubiquitous feature of HOIs that constrain neural activity patterns and can influence information processing.

From grid cells and visual place cells to multimodal place cell: a new robotic architecture

In the present study, a new architecture for the generation of grid cells (GC) was implemented on a real robot. In order to test this model a simple place cell (PC) model merging visual PC activity and GC was developed. GC were first built from a simple "several to one" projection (similar to a modulo operation) performed on a neural field coding for path integration (PI). Robotics experiments raised several practical and theoretical issues. To limit the important angular drift of PI, head direction information was introduced in addition to the robot proprioceptive signal coming from the wheel rotation. Next, a simple associative learning between visual place cells and the neural field coding for the PI has been used to recalibrate the PI and to limit its drift. Finally, the parameters controlling the shape of the PC built from the GC have been studied. Increasing the number of GC obviously improves the shape of the resulting place field. Yet, other parameters such as the discretization factor of PI or the lateral interactions between GC can have an important impact on the place field quality and avoid the need of a very large number of GC. In conclusion, our results show our GC model based on the compression of PI is congruent with neurobiological studies made on rodent. GC firing patterns can be the result of a modulo transformation of PI information. We argue that such a transformation may be a general property of the connectivity from the cortex to the entorhinal cortex. Our model predicts that the effect of similar transformations on other kinds of sensory information (visual, tactile, auditory, etc…) in the entorhinal cortex should be observed. Consequently, a given EC cell should react to non-contiguous input configurations in non-spatial conditions according to the projection from its different inputs.

Place cells, grid cells, and memory

The hippocampal system is critical for storage and retrieval of declarative memories, including memories for locations and events that take place at those locations. Spatial memories place high demands on capacity. Memories must be distinct to be recalled without interference and encoding must be fast. Recent studies have indicated that hippocampal networks allow for fast storage of large quantities of uncorrelated spatial information. The aim of the this article is to review and discuss some of this work, taking as a starting point the discovery of multiple functionally specialized cell types of the hippocampal-entorhinal circuit, such as place, grid, and border cells. We will show that grid cells provide the hippocampus with a metric, as well as a putative mechanism for decorrelation of representations, that the formation of environment-specific place maps depends on mechanisms for long-term plasticity in the hippocampus, and that long-term spatiotemporal memory storage may depend on offline consolidation processes related to sharp-wave ripple activity in the hippocampus. The multitude of representations generated through interactions between a variety of functionally specialized cell types in the entorhinal-hippocampal circuit may be at the heart of the mechanism for declarative memory formation.

Beyond the bolus: transgenic tools for investigating the neurophysiology of learning and memory

Understanding the neural mechanisms underlying learning and memory in the entorhinal-hippocampal circuit is a central challenge of systems neuroscience. For more than 40 years, electrophysiological recordings in awake, behaving animals have been used to relate the receptive fields of neurons in this circuit to learning and memory. However, the vast majority of such studies are purely observational, as electrical, surgical, and pharmacological circuit manipulations are both challenging and relatively coarse, being unable to distinguish between specific classes of neurons. Recent advances in molecular genetic tools can overcome many of these limitations, enabling unprecedented control over neural activity in behaving animals. Expression of pharmaco- or optogenetic transgenes in cell-type-specific "driver" lines provides unparalleled anatomical and cell-type specificity, especially when delivered by viral complementation. Pharmacogenetic transgenes are specially designed neurotransmitter receptors exclusively activated by otherwise inactive synthetic ligands and have kinetics similar to traditional pharmacology. Optogenetic transgenes use light to control the membrane potential, and thereby operate at the millisecond timescale. Thus, activation of pharmacogenetic transgenes in specific neuronal cell types while recording from other parts of the circuit allows investigation of the role of those neurons in the steady state, whereas optogenetic transgenes allow one to determine the immediate network response.

Modeling inheritance of phase precession in the hippocampal formation

Spatial information about the environment is encoded by the activity of place and grid cells in the hippocampal formation. As an animal traverses a cell's firing field, action potentials progressively shift to earlier phases of the theta oscillation (6-10 Hz). This "phase precession" is observed also in the prefrontal cortex and the ventral striatum, but mechanisms for its generation are unknown. However, once phase precession exists in one region, it might also propagate to downstream regions. Using a computational model, we analyze such inheritance of phase precession, for example, from the entorhinal cortex to CA1 and from CA3 to CA1. We find that distinctive subthreshold and suprathreshold features of the membrane potential of CA1 pyramidal cells (Harvey et al., 2009; Mizuseki et al., 2012; Royer et al., 2012) can be explained by inheritance and that excitatory input is essential. The model explains how inhibition modulates the slope and range of phase precession and provides two main testable predictions. First, theta-modulated inhibitory input to a CA1 pyramidal cell is not necessary for phase precession. Second, theta-modulated inhibitory input on its own generates membrane potential peaks that are in phase with peaks of the extracellular field. Furthermore, we suggest that the spatial distribution of field centers of a population of phase-precessing input cells determines, not only the place selectivity, but also the characteristics of phase precession of the targeted output cell. The inheritance model thus can explain why phase precession is observed throughout the hippocampal formation and other areas of the brain.

Sparse encoding of automatic visual association in hippocampal networks

Intelligent action entails exploiting predictions about associations between elements of ones environment. The hippocampus and mediotemporal cortex are endowed with the network topology, physiology, and neurochemistry to automatically and sparsely code sensori-cognitive associations that can be reconstructed from single or partial inputs. Whilst acquiring fMRI data and performing an attentional task, participants were incidentally presented with a sequence of cartoon images. By assigning subjects a post-scan free-association task on the same images we assayed the density of associations triggered by these stimuli. Using multivariate Bayesian decoding, we show that human hippocampal and temporal neocortical structures host sparse associative representations that are automatically triggered by visual input. Furthermore, as predicted theoretically, there was a significant increase in sparsity in the Cornu Ammonis subfields, relative to the entorhinal cortex. Remarkably, the sparsity of CA encoding correlated significantly with associative memory performance over subjects; elsewhere within the temporal lobe, entorhinal, parahippocampal, perirhinal and fusiform cortices showed the highest model evidence for the sparse encoding of associative density. In the absence of reportability or attentional confounds, this charts a distribution of visual associative representations within hippocampal populations and their temporal lobe afferent fields, and demonstrates the viability of retrospective associative sampling techniques for assessing the form of reflexive associative encoding.

New and distinct hippocampal place codes are generated in a new environment during septal inactivation

The hippocampus generates distinct neural codes to disambiguate similar experiences, a process thought to underlie episodic memory function. Entorhinal grid cells provide a prominent spatial signal to hippocampus, and changes in their firing pattern could thus generate a distinct spatial code in each context. We examined whether we would preclude the emergence of new spatial representations in a novel environment during muscimol inactivation of the medial septal area, a manipulation known to disrupt theta oscillations and grid cell firing. We found that new, highly distinct configurations of place fields emerged immediately and remained stable during the septal inactivation. The new place code persisted when theta oscillations had recovered. Theta rhythmicity and feedforward input from grid cell networks were thus not required to generate new spatial representations in the hippocampus.

Mechanisms of hippocampal astrocytes mediation of spatial memory and theta rhythm by gliotransmitters and growth factors

Our knowledge about encoding and maintenance of spatial memory emphasizes the integrated functional role of the grid cells and the place cells of the hippocampus in the generation of theta rhythm in spatial memory formation. However, the role of astrocytes in these processes is often underestimated in their contribution to the required structural and functional characteristics of hippocampal neural network operative in spatial memory. We show that hippocampal astrocytes, by the secretion of gliotransmitters, such as glutamate, d-serine, and ATP and growth factors such as BDNF and by the expression of receptors and channels such as those of TNFα and aquaporin, have several diverse fuctions in spatial memory. We specifically focus on the role of astrocytes on five phases of spatial memory: (1) theta rhythm generation, (2) theta phase precession, (3) formation of spatial memory by mapping data of entorhinal grid cells into the place cells, (4) storage of spatial information, and (5) maintenance of spatial memory. Finally, by reviewing the literature, we propose specific mechanisms mentioned in the form of a hypothesis suggesting that astrocytes are important in spatial memory formation.

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.

The transformation from grid cells to place cells is robust to noise in the grid pattern

Spatial navigation in rodents has been attributed to place-selective cells in the hippocampus and entorhinal cortex. However, there is currently no consensus on the neural mechanisms that generate the place-selective activity in hippocampal place cells or entorhinal grid cells. Given the massive input connections from the superficial layers of the entorhinal cortex to place cells in the hippocampal cornu ammonis (CA) regions, it was initially postulated that grid cells drive the spatial responses of place cells. However, recent experiments have found that place cell responses are stable even when grid cell responses are severely distorted, thus suggesting that place cells cannot receive their spatial information chiefly from grid cells. Here, we offer an alternative explanation. In a model with linear grid-to-place-cell transformation, the transformation can be very robust against noise in the grid patterns depending on the nature of the noise. In the two more realistic noise scenarios, the transformation was very robust, while it was not in the other two scenarios. Although current experimental data suggest that other types of place-selective cells modulate place cell responses, our results show that the simple grid-to-place-cell transformation alone can account for the origin of place selectivity in the place cells.

Representation of geometric borders in the developing rat

Local space is represented by a number of functionally specific cell types, including place cells in the hippocampus and grid cells, head direction cells, and border cells in the medial entorhinal cortex (MEC). These cells form a functional map of external space already at the time when rat pups leave the nest for the first time in their life, at the age of 2.5 weeks. However, while place cells have adult-like firing fields from the outset, grid cells have irregular and variable fields until the fourth week, raising doubts about their contribution to place cell firing at young age. Recording in MEC of juvenile rats, we show that, unlike grid cells, border cells express adult-like firing fields from the first days of exposure to an open environment, at postnatal days 16-18. Thus, spatial signals from border cells may be sufficient to maintain spatially localized firing in juvenile hippocampal place cells.

Space in the brain: how the hippocampal formation supports spatial cognition

Over the past four decades, research has revealed that cells in the hippocampal formation provide an exquisitely detailed representation of an animal's current location and heading. These findings have provided the foundations for a growing understanding of the mechanisms of spatial cognition in mammals, including humans. We describe the key properties of the major categories of spatial cells: place cells, head direction cells, grid cells and boundary cells, each of which has a characteristic firing pattern that encodes spatial parameters relating to the animal's current position and orientation. These properties also include the theta oscillation, which appears to play a functional role in the representation and processing of spatial information. Reviewing recent work, we identify some themes of current research and introduce approaches to computational modelling that have helped to bridge the different levels of description at which these mechanisms have been investigated. These range from the level of molecular biology and genetics to the behaviour and brain activity of entire organisms. We argue that the neuroscience of spatial cognition is emerging as an exceptionally integrative field which provides an ideal test-bed for theories linking neural coding, learning, memory and cognition.

What do grid cells contribute to place cell firing?

The unitary firing fields of hippocampal place cells are commonly assumed to be generated by input from entorhinal grid cell modules with differing spatial scales. Here, we review recent research that brings this assumption into doubt. Instead, we propose that place cell spatial firing patterns are determined by environmental sensory inputs, including those representing the distance and direction to environmental boundaries, while grid cells provide a complementary self-motion related input that contributes to maintaining place cell firing. In this view, grid and place cell firing patterns are not successive stages of a processing hierarchy, but complementary and interacting representations that work in combination to support the reliable coding of large-scale space.

Grid cells and neural coding in high-end cortices

An ultimate goal of neuroscience is to understand the mechanisms of mammalian intellectual functions, many of which are thought to depend extensively on the cerebral cortex. While this may have been considered a remote objective when Neuron was launched in 1988, neuroscience has now evolved to a stage where it is possible to decipher neural-circuit mechanisms in the deepest parts of the cortex, far away from sensory receptors and motoneurons. In this review, we show how studies of place cells in the hippocampus and grid cells in the entorhinal cortex may provide some of the first glimpses into these mechanisms. We shall review the events that led up to the discovery of grid cells and a functional circuit in the entorhinal cortex and highlight what we currently see as the big questions in this field--questions that, if resolved, will add to our understanding of cortical computation in a general sense.

Limbic systems for emotion and for memory, but no single limbic system

The concept of a (single) limbic system is shown to be outmoded. Instead, anatomical, neurophysiological, functional neuroimaging, and neuropsychological evidence is described that anterior limbic and related structures including the orbitofrontal cortex and amygdala are involved in emotion, reward valuation, and reward-related decision-making (but not memory), with the value representations transmitted to the anterior cingulate cortex for action-outcome learning. In this 'emotion limbic system' a computational principle is that feedforward pattern association networks learn associations from visual, olfactory and auditory stimuli, to primary reinforcers such as taste, touch, and pain. In primates including humans this learning can be very rapid and rule-based, with the orbitofrontal cortex overshadowing the amygdala in this learning important for social and emotional behaviour. Complementary evidence is described showing that the hippocampus and limbic structures to which it is connected including the posterior cingulate cortex and the fornix-mammillary body-anterior thalamus-posterior cingulate circuit are involved in episodic or event memory, but not emotion. This 'hippocampal system' receives information from neocortical areas about spatial location, and objects, and can rapidly associate this information together by the different computational principle of autoassociation in the CA3 region of the hippocampus involving feedback. The system can later recall the whole of this information in the CA3 region from any component, a feedback process, and can recall the information back to neocortical areas, again a feedback (to neocortex) recall process. Emotion can enter this memory system from the orbitofrontal cortex etc., and be recalled back to the orbitofrontal cortex etc. during memory recall, but the emotional and hippocampal networks or 'limbic systems' operate by different computational principles, and operate independently of each other except insofar as an emotional state or reward value attribute may be part of an episodic memory.

Parallel and convergent processing in grid cell, head-direction cell, boundary cell, and place cell networks

The brain is able to construct internal representations that correspond to external spatial coordinates. Such brain maps of the external spatial topography may support a number of cognitive functions, including navigation and memory. The neuronal building block of brain maps are place cells, which are found throughout the hippocampus of rodents and, in a lower proportion, primates. Place cells typically fire in one or few restricted areas of space, and each area where a cell fires can range, along the dorsoventral axis of the hippocampus, from 30 cm to at least several meters. The sensory processing streams that give rise to hippocampal place cells are not fully understood, but substantial progress has been made in characterizing the entorhinal cortex, which is the gateway between neocortical areas and the hippocampus. Entorhinal neurons have diverse spatial firing characteristics, and the different entorhinal cell types converge in the hippocampus to give rise to a single, spatially modulated cell type—the place cell. We therefore suggest that parallel information processing in different classes of cells—as is typically observed at lower levels of sensory processing—continues up into higher level association cortices, including those that provide the inputs to hippocampus. WIREs Cogn Sci 2014, 5:207–219. doi: 10.1002/wcs.1272

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

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

Functional connectivity of the entorhinal-hippocampal space circuit

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

Architecture of spatial circuits in the hippocampal region

The hippocampal region contains several principal neuron types, some of which show distinct spatial firing patterns. The region is also known for its diversity in neural circuits and many have attempted to causally relate network architecture within and between these unique circuits to functional outcome. Still, much is unknown about the mechanisms or network properties by which the functionally specific spatial firing profiles of neurons are generated, let alone how they are integrated into a coherently functioning meta-network. In this review, we explore the architecture of local networks and address how they may interact within the context of an overarching space circuit, aiming to provide directions for future successful explorations.

Does the entorhinal cortex use the Fourier transform?

Some neurons in the entorhinal cortex (EC) fire bursts when the animal occupies locations organized in a hexagonal grid pattern in their spatial environment. Place cells have also been observed, firing bursts only when the animal occupies a particular region of the environment. Both of these types of cells exhibit theta-cycle modulation, firing bursts in the 4–12 Hz range. Grid cells fire bursts of action potentials that precess with respect to the theta cycle, a phenomenon dubbed “theta precession.” Various models have been proposed to explain these phenomena, and how they relate to navigation. Among the most promising are the oscillator interference models. The bank-of-oscillators model proposed by Welday et al. (2011) exhibits all these features. However, their simulations are based on theoretical oscillators, and not implemented entirely with spiking neurons. We extend their work in a number of ways. First, we place the oscillators in a frequency domain and reformulate the model in terms of Fourier theory. Second, this perspective suggests a division of labor for implementing spatial maps: position vs. map layout. The animal's position is encoded in the phases of the oscillators, while the spatial map shape is encoded implicitly in the weights of the connections between the oscillators and the read-out nodes. Third, it reveals that the oscillator phases all need to conform to a linear relationship across the frequency domain. Fourth, we implement a partial model of the EC using spiking leaky integrate-and-fire (LIF) neurons. Fifth, we devise new coupling mechanisms, enlightened by the global phase constraint, and show they are capable of keeping spiking neural oscillators in consistent formation. Our model demonstrates place cells, grid cells, and phase precession. The Fourier model also gives direction for future investigations, such as integrating sensory feedback to combat drift, or explaining why grid cells exist at all.

The mechanisms for pattern completion and pattern separation in the hippocampus

The mechanisms for pattern completion and pattern separation are described in the context of a theory of hippocampal function in which the hippocampal CA3 system operates as a single attractor or autoassociation network to enable rapid, one-trial, associations between any spatial location (place in rodents, or spatial view in primates) and an object or reward, and to provide for completion of the whole memory during recall from any part. The factors important in the pattern completion in CA3 together with a large number of independent memories stored in CA3 include a sparse distributed representation which is enhanced by the graded firing rates of CA3 neurons, representations that are independent due to the randomizing effect of the mossy fibers, heterosynaptic long-term depression as well as long-term potentiation in the recurrent collateral synapses, and diluted connectivity to minimize the number of multiple synapses between any pair of CA3 neurons which otherwise distort the basins of attraction. Recall of information from CA3 is implemented by the entorhinal cortex perforant path synapses to CA3 cells, which in acting as a pattern associator allow some pattern generalization. Pattern separation is performed in the dentate granule cells using competitive learning to convert grid-like entorhinal cortex firing to place-like fields. Pattern separation in CA3, which is important for completion of any one of the stored patterns from a fragment, is provided for by the randomizing effect of the mossy fiber synapses to which neurogenesis may contribute, by the large number of dentate granule cells each with a sparse representation, and by the sparse independent representations in CA3. Recall to the neocortex is achieved by a reverse hierarchical series of pattern association networks implemented by the hippocampo-cortical backprojections, each one of which performs some pattern generalization, to retrieve a complete pattern of cortical firing in higher-order cortical areas.

Impaired hippocampal rate coding after lesions of the lateral entorhinal cortex

In the hippocampus, spatial and non-spatial parameters may be represented by a dual coding scheme, in which coordinates in space are expressed by the collective firing locations of place cells and the diversity of experience at these locations is encoded by orthogonal variations in firing rates. Although the spatial signal may reflect input from medial entorhinal cortex, the sources of the variations in firing rate have not been identified. We found that rate variations in rat CA3 place cells depended on inputs from the lateral entorhinal cortex (LEC). Hippocampal rate remapping, induced by changing the shape or the color configuration of the environment, was impaired by lesions in those parts of the ipsilateral LEC that provided the densest input to the hippocampal recording position. Rate remapping was not observed in LEC itself. The findings suggest that LEC inputs are important for efficient rate coding in the hippocampus.

A quantitative theory of the functions of the hippocampal CA3 network in memory

A quantitative computational theory of the operation of the hippocampal CA3 system as an autoassociation or attractor network used in episodic memory system is described. In this theory, the CA3 system operates as a single attractor or autoassociation network to enable rapid, one-trial, associations between any spatial location (place in rodents, or spatial view in primates) and an object or reward, and to provide for completion of the whole memory during recall from any part. The theory is extended to associations between time and object or reward to implement temporal order memory, also important in episodic memory. The dentate gyrus (DG) performs pattern separation by competitive learning to produce sparse representations suitable for setting up new representations in CA3 during learning, producing for example neurons with place-like fields from entorhinal cortex grid cells. The dentate granule cells produce by the very small number of mossy fiber (MF) connections to CA3 a randomizing pattern separation effect important during learning but not recall that separates out the patterns represented by CA3 firing to be very different from each other, which is optimal for an unstructured episodic memory system in which each memory must be kept distinct from other memories. The direct perforant path (pp) input to CA3 is quantitatively appropriate to provide the cue for recall in CA3, but not for learning. Tests of the theory including hippocampal subregion analyses and hippocampal NMDA receptor knockouts are described, and support the theory.

Optogenetic dissection of entorhinal-hippocampal functional connectivity

We used a combined optogenetic-electrophysiological strategy to determine the functional identity of entorhinal cells with output to the place-cell population in the hippocampus. Channelrhodopsin-2 (ChR2) was expressed selectively in the hippocampus-targeting subset of entorhinal projection neurons by infusing retrogradely transportable ChR2-coding recombinant adeno-associated virus in the hippocampus. Virally transduced ChR2-expressing cells were identified in medial entorhinal cortex as cells that fired at fixed minimal latencies in response to local flashes of light. A large number of responsive cells were grid cells, but short-latency firing was also induced in border cells and head-direction cells, as well as cells with irregular or nonspatial firing correlates, which suggests that place fields may be generated by convergence of signals from a broad spectrum of entorhinal functional cell types.

Spiking neurons in a hierarchical self-organizing map model can learn to develop spatial and temporal properties of entorhinal grid cells and hippocampal place cells

Medial entorhinal grid cells and hippocampal place cells provide neural correlates of spatial representation in the brain. A place cell typically fires whenever an animal is present in one or more spatial regions, or places, of an environment. A grid cell typically fires in multiple spatial regions that form a regular hexagonal grid structure extending throughout the environment. Different grid and place cells prefer spatially offset regions, with their firing fields increasing in size along the dorsoventral axes of the medial entorhinal cortex and hippocampus. The spacing between neighboring fields for a grid cell also increases along the dorsoventral axis. This article presents a neural model whose spiking neurons operate in a hierarchy of self-organizing maps, each obeying the same laws. This spiking GridPlaceMap model simulates how grid cells and place cells may develop. It responds to realistic rat navigational trajectories by learning grid cells with hexagonal grid firing fields of multiple spatial scales and place cells with one or more firing fields that match neurophysiological data about these cells and their development in juvenile rats. The place cells represent much larger spaces than the grid cells, which enable them to support navigational behaviors. Both self-organizing maps amplify and learn to categorize the most frequent and energetic co-occurrences of their inputs. The current results build upon a previous rate-based model of grid and place cell learning, and thus illustrate a general method for converting rate-based adaptive neural models, without the loss of any of their analog properties, into models whose cells obey spiking dynamics. New properties of the spiking GridPlaceMap model include the appearance of theta band modulation. The spiking model also opens a path for implementation in brain-emulating nanochips comprised of networks of noisy spiking neurons with multiple-level adaptive weights for controlling autonomous adaptive robots capable of spatial navigation.

The hippocampo-cortical loop: spatio-temporal learning and goal-oriented planning in navigation

We present a neural network model where the spatial and temporal components of a task are merged and learned in the hippocampus as chains of associations between sensory events. The prefrontal cortex integrates this information to build a cognitive map representing the environment. The cognitive map can be used after latent learning to select optimal actions to fulfill the goals of the animal. A simulation of the architecture is made and applied to learning and solving tasks that involve both spatial and temporal knowledge. We show how this model can be used to solve the continuous place navigation task, where a rat has to navigate to an unmarked goal and wait for 2 seconds without moving to receive a reward. The results emphasize the role of the hippocampus for both spatial and timing prediction, and the prefrontal cortex in the learning of goals related to the task.

The operation of pattern separation and pattern completion processes associated with different attributes or domains of memory

Pattern separation and pattern completion processes are central to how the brain processes information in an efficient manner. Research into these processes is escalating and deficient pattern separation is being implicated in a wide array of genetic disorders as well as in neurocognitive aging. Despite the quantity of research, there remains a controversy as to precisely which behavioral paradigms should be used to best tap into pattern separation and pattern completion processes, as well as to what constitute legitimate outcome measures reflecting impairments in pattern separation and pattern completion. This review will discuss a theory based on multiple memory systems that provides a framework upon which behavioral tasks can be designed and their results interpreted. Furthermore, this review will discuss the nature of pattern separation and pattern completion and extend these processes outside the hippocampus and across all domains of information processing. After these discussions, an optimal strategy for designing behavioral paradigms to evaluate pattern separation and pattern completion processes will be provided.

How entorhinal grid cells may learn multiple spatial scales from a dorsoventral gradient of cell response rates in a self-organizing map

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

Spatial navigation is a critical competence of all higher mammals, and place cells in the hippocampus represent the large spaces in which they navigate. Recent modeling clarifies how this may occur via interactions between grid cells in the medial entorhinal cortex (MEC) and place cells. Grid cells exhibit hexagonal grid firing patterns across space and come in multiple spatial scales that increase along the dorsoventral axis of MEC. Signals from multiple scales of grid cells combine to activate place cells that represent much larger spaces than grid cells. This article shows how a gradient of cell response rates along the dorsoventral axis enables the learning of grid cells with the observed gradient of spatial scales as an animal navigates realistic trajectories. The observed gradient of grid cell membrane potential oscillation frequencies is shown to be a direct result of the gradient of response rates. This gradient mechanism for spatial learning is homologous to a gradient mechanism for temporal learning in the lateral entorhinal cortex and its hippocampal projections, thereby clarifying why both spatial and temporal representations are found in the entorhinal-hippocampal system.

Grid alignment in entorhinal cortex

The spatial responses of many of the cells recorded in all layers of rodent medial entorhinal cortex (mEC) show mutually aligned grid patterns. Recent experimental findings have shown that grids can often be better described as elliptical rather than purely circular and that, beyond the mutual alignment of their grid axes, ellipses tend to also orient their long axis along preferred directions. Are grid alignment and ellipse orientation aspects of the same phenomenon? Does the grid alignment result from single-unit mechanisms or does it require network interactions? We address these issues by refining a single-unit adaptation model of grid formation, to describe specifically the spontaneous emergence of conjunctive grid-by-head-direction cells in layers III, V, and VI of mEC. We find that tight alignment can be produced by recurrent collateral interactions, but this requires head-direction (HD) modulation. Through a competitive learning process driven by spatial inputs, grid fields then form already aligned, and with randomly distributed spatial phases. In addition, we find that the self-organization process is influenced by any anisotropy in the behavior of the simulated rat. The common grid alignment often orients along preferred running directions (RDs), as induced in a square environment. When speed anisotropy is present in exploration behavior, the shape of individual grids is distorted toward an ellipsoid arrangement. Speed anisotropy orients the long ellipse axis along the fast direction. Speed anisotropy on its own also tends to align grids, even without collaterals, but the alignment is seen to be loose. Finally, the alignment of spatial grid fields in multiple environments shows that the network expresses the same set of grid fields across environments, modulo a coherent rotation and translation. Thus, an efficient metric encoding of space may emerge through spontaneous pattern formation at the single-unit level, but it is coherent, hence context-invariant, if aided by collateral interactions.

Invariant Visual Object and Face Recognition: Neural and Computational Bases, and a Model, VisNet

Neurophysiological evidence for invariant representations of objects and faces in the primate inferior temporal visual cortex is described. Then a computational approach to how invariant representations are formed in the brain is described that builds on the neurophysiology. A feature hierarchy model in which invariant representations can be built by self-organizing learning based on the temporal and spatial statistics of the visual input produced by objects as they transform in the world is described. VisNet can use temporal continuity in an associative synaptic learning rule with a short-term memory trace, and/or it can use spatial continuity in continuous spatial transformation learning which does not require a temporal trace. The model of visual processing in the ventral cortical stream can build representations of objects that are invariant with respect to translation, view, size, and also lighting. The model has been extended to provide an account of invariant representations in the dorsal visual system of the global motion produced by objects such as looming, rotation, and object-based movement. The model has been extended to incorporate top-down feedback connections to model the control of attention by biased competition in, for example, spatial and object search tasks. The approach has also been extended to account for how the visual system can select single objects in complex visual scenes, and how multiple objects can be represented in a scene. The approach has also been extended to provide, with an additional layer, for the development of representations of spatial scenes of the type found in the hippocampus.

Optimal population codes for space: grid cells outperform place cells

Rodents use two distinct neuronal coordinate systems to estimate their position: place fields in the hippocampus and grid fields in the entorhinal cortex. Whereas place cells spike at only one particular spatial location, grid cells fire at multiple sites that correspond to the points of an imaginary hexagonal lattice. We study how to best construct place and grid codes, taking the probabilistic nature of neural spiking into account. Which spatial encoding properties of individual neurons confer the highest resolution when decoding the animal's position from the neuronal population response? A priori, estimating a spatial position from a grid code could be ambiguous, as regular periodic lattices possess translational symmetry. The solution to this problem requires lattices for grid cells with different spacings; the spatial resolution crucially depends on choosing the right ratios of these spacings across the population. We compute the expected error in estimating the position in both the asymptotic limit, using Fisher information, and for low spike counts, using maximum likelihood estimation. Achieving high spatial resolution and covering a large range of space in a grid code leads to a trade-off: the best grid code for spatial resolution is built of nested modules with different spatial periods, one inside the other, whereas maximizing the spatial range requires distinct spatial periods that are pairwisely incommensurate. Optimizing the spatial resolution predicts two grid cell properties that have been experimentally observed. First, short lattice spacings should outnumber long lattice spacings. Second, the grid code should be self-similar across different lattice spacings, so that the grid field always covers a fixed fraction of the lattice period. If these conditions are satisfied and the spatial "tuning curves" for each neuron span the same range of firing rates, then the resolution of the grid code easily exceeds that of the best possible place code with the same number of neurons.

The hippocampus and visual perception

In this review, we will discuss the idea that the hippocampus may be involved in both memory and perception, contrary to theories that posit functional and neuroanatomical segregation of these processes. This suggestion is based on a number of recent neuropsychological and functional neuroimaging studies that have demonstrated that the hippocampus is involved in the visual discrimination of complex spatial scene stimuli. We argue that these findings cannot be explained by long-term memory or working memory processing or, in the case of patient findings, dysfunction beyond the medial temporal lobe (MTL). Instead, these studies point toward a role for the hippocampus in higher-order spatial perception. We suggest that the hippocampus processes complex conjunctions of spatial features, and that it may be more appropriate to consider the representations for which this structure is critical, rather than the cognitive processes that it mediates.