Place Cell Function Research Articles

Distinct Disruptions in CA1 and CA3 Place Cell Function in Alzheimer's Disease Mice.

The hippocampus, a critical brain structure for spatial learning and memory, is susceptible to neurodegenerative disorders such as Alzheimer's disease (AD). The APPswe/PSEN1dE9 (APP/PS1) transgenic mouse model is widely used to study the pathology of AD. Although previous research has established AD-associated impairments in hippocampal-dependent learning and memory, the neurophysiological mechanisms underlying these cognitive dysfunctions remain less understood. To address this gap, we investigated the activities of place cells in both CA1 and CA3 hippocampal subregions, which have distinct yet complementary computational roles. Behaviorally, APP/PS1 mice demonstrated impaired spatial recognition memory compared to wild-type (WT) mice in the object location test. Physiologically, place cells in APP/PS1 mice showed deterioration in spatial representation compared to WT. Specifically, CA1 place cells exhibited significant reductions in coherence and spatial information, while CA3 place cells displayed a significant reduction in place field size. Both CA1 and CA3 place cells in APP/PS1 mice also showed significant disruptions in their ability to stably encode the same environment. Furthermore, the burst firing properties of these cells were altered to forms correlated with reduced cognition. Additionally, the theta rhythm was significantly attenuated in CA1 place cells of APP/PS1 mice compared to WT. Our results suggest that distinct alteration in the physiological properties of CA1 and CA3 place cells, coupled with disrupted hippocampal theta rhythm in CA1, may collectively contribute to impaired hippocampal-dependent spatial learning and memory in AD.

Hippocampal place cells exhibit impairments in spatial information coding in an APP‐knock in model of Alzheimer’s Disease

AbstractBackgroundThe hippocampus is one of the first cortical regions to exhibit Alzheimer’s Disease (AD) pathology. The spatially‐related firing of hippocampal place cells provides the cellular basis for spatial memory and this is impaired relatively early in AD, yet few studies examine place cell activity in AD mouse models. We undertook an in‐depth characterisation of hippocampal pyramidal cell activity in an APP knock‐in model of AD.MethodMicrodrives carrying 8 tetrodes were implanted into the left CA1 hippocampal subfield of 14‐month female mice (four APPNL‐G‐F, four wild‐type littermates). Mice undertook daily foraging sessions in an open field and ran on a linear track as CA1 activity was recorded. On completion of experiments, 40µm coronal sections of fixed brain tissue were stained using Cresyl Violet to verify tetrode placement and Thioflavin‐S to quantify amyloid β plaque burden.Result270 cells were recorded (119 wild‐type, 151 APPNL‐G‐F). CA1 pyramidal cells were classified as “place cells” or “non‐place cells” based on their spatial information content (bits/spike). Pyramidal cells recorded in wild‐type mice had significantly greater spatial information content than APPNL‐G‐F pyramidal cells (Figure 1), and a higher proportion were classified as place cells. Wild‐type place cells had significantly greater spatial information content than APPNL‐G‐F place cells. Corresponding differences were identified in place field size (Figures 2, 3). A positive correlation was identified between Aβ plaque burden and pyramidal cell spatial information content in the four APPNL‐G‐F mice (Figure 4).Coordination of place cell firing by the hippocampal theta rhythm was disrupted in APPNL‐G‐F mice; phase‐locking of pyramidal cells and place cells was significantly reduced (Figure 5), there was less coherence in the preferred theta phase across the pyramidal cell population, and a reduction in theta phase precession was observed.ConclusionAPPNL‐G‐F CA1 place cells exhibited deficits in both rate coding and temporal coding of spatial information indicating that amyloid β pathology, in the absence tau tangles, was associated with a disruption of hippocampal place cell function. These results provide preliminary support for the hypothesis that disruption of hippocampal function by AD pathology manifests as altered place cell activity and spatial behaviour.

A computational grid-to-place-cell transformation model indicates a synaptic driver of place cell impairment in early-stage Alzheimer's Disease.

Alzheimer’s Disease (AD) is characterized by progressive neurodegeneration and cognitive impairment. Synaptic dysfunction is an established early symptom, which correlates strongly with cognitive decline, and is hypothesised to mediate the diverse neuronal network abnormalities observed in AD. However, how synaptic dysfunction contributes to network pathology and cognitive impairment in AD remains elusive. Here, we present a grid-cell-to-place-cell transformation model of long-term CA1 place cell dynamics to interrogate the effect of synaptic loss on network function and environmental representation. Synapse loss modelled after experimental observations in the APP/PS1 mouse model was found to induce firing rate alterations and place cell abnormalities that have previously been observed in AD mouse models, including enlarged place fields and lower across-session stability of place fields. Our results support the hypothesis that synaptic dysfunction underlies cognitive deficits, and demonstrate how impaired environmental representation may arise in the early stages of AD. We further propose that dysfunction of excitatory and inhibitory inputs to CA1 pyramidal cells may cause distinct impairments in place cell function, namely reduced stability and place map resolution.

Cognitive mapping: Anchoring our brain’s sense of space in a dynamic world

In complex environments, we rely on knowing our current location and pathways to others in order to flexibly navigate. But viable routes often change. New research suggests how the brain tracks where we are regardless of paths available to us.

Is hippocampal remapping the physiological basis for context?

In 1980, Nadel and Wilner extended Richard Hirsh's notion that the hippocampus creates environmental representations, called "contexts," suggesting that the fundamental structure of context was the spatial representation proposed by O'Keefe and Nadel's landmark book, The Hippocampus as a Cognitive Map (1978). This book, in turn, derives from the discovery that individual hippocampal neurons act as place cells, with the complete set of place cells tiling an enclosure, forming a type of spatial map. It was found that unique environments had unique place cell representations. That is, if one takes the hippocampal map of a specific environment, this representation scrambles, or "remaps" when the animal is placed in a different environment. Several authors have speculated that "maps" and "remapping" form the physiological substrates for context and context shifting. One difficulty with this definition is that it is exclusively spatial; it can only be inferred when an animal locomotes in an enclosure. There are five aims for this article. The first is to give an historical overview of context as a variable that controls behavior. The second aim is to give an historical overview of concepts of place cell maps and remapping. The third aim is to propose an updated definition of a place cell map, based on temporal rather than spatial overlaps, which adds flexibility. The fourth aim is to address the issue of whether the biological phenomenon of hippocampal remapping, is, in fact, the substrate for shifts in the psychological phenomenon of context. The final aim is speculation of how contextual representations may contribute to effective behavior.

In vivo Calcium Imaging Reveals That Cortisol Treatment Reduces the Number of Place Cells in Thy1-GCaMP6f Transgenic Mice.

The hippocampus, a structure essential for spatial navigation and memory undergoes anatomical and functional changes during chronic stress. Here, we investigate the effects of chronic stress on the ability of place cells to encode the neural representation of a linear track. To model physiological conditions of chronic stress on hippocampal function, transgenic mice expressing the genetically encoded calcium indicator GCaMP6f in CA1 pyramidal neurons were chronically administered with 40 μg/ml of cortisol for 8 weeks. Cortisol-treated mice exhibited symptoms typically observed during chronic stress, including diminished reward seeking behavior and reduced adrenal gland and spleen weights. In vivo imaging of hippocampal cellular activity during linear track running behavior revealed a reduced number of cells that could be recruited to encode spatial position, despite an unchanged overall number of active cells, in cortisol-treated mice. The properties of the remaining place cells that could be recruited to encode spatial information, however, was unperturbed. Bayesian decoders trained to estimate the mouse’s position on the track using single neuron activity data demonstrated reduced performance in a cue richness-dependent fashion in cortisol-treated animals. The performance of decoders utilizing data from the entire neuronal ensemble was unaffected by cortisol treatment. Finally, to test the hypothesis that an antidepressant drug could prevent the effects of cortisol, we orally administered a group of mice with 10 mg/kg citalopram during cortisol administration. Citalopram prevented the cortisol-induced decrease in single-neuron decoder performance but failed to significantly prevent anhedonia and the cortisol-induced reduction in the proportion place cells. The dysfunction observed at the single-neuron level indicates that chronic stress may impair the ability of the hippocampus to encode individual neural representations of the mouse’s spatial position, a function pivotal to forming an accurate navigational map of the mouse’s external environment; however, the hippocampal ensemble as a whole is resilient to any cortisol-induced insults to single neuronal place cell function on the linear track.

Hippocampal LTP modulation and glutamatergic receptors following vestibular loss.

Vestibular dysfunction strongly impairs hippocampus-dependent spatial memory performance and place cell function. However, the hippocampal encoding of vestibular information at the synaptic level, remains sparsely explored and controversial. We investigated changes in in vivo long-term potentiation (LTP) and NMDA glutamate receptor (NMDAr) density and distribution after bilateral vestibular lesions (BVL) in adult rats. At day 30 (D30) post-BVL, the LTP of the population spike recorded in the dentate gyrus (DG) was higher in BVL rats, for the entire 3h of LTP recording, while no difference was observed in the fEPSP slope. However, there was an increase in EPSP-spike (E-S) potentiation in lesioned rats. NMDArs were upregulated at D7 and D30 predominantly within the DG and CA1. At D30, we observed a higher NMDAr density in the left hippocampus. NMDArs were overexpressed on both neurons and non-neuronal cells, suggesting a decrease of the entorhinal glutamatergic inputs to the hippocampus following BVL. The EPSP-spike (E-S) potentiation increase was consistent with the dorsal hippocampus NMDAr upregulation. Such an increase could reflect a non-specific enhancement of synaptic efficacy, leading to a disruption of memory encoding, and therefore might underlie the memory deficits previously reported in rats and humans following vestibular loss.

The modulation of hippocampal theta rhythm by the vestibular system.

The vestibular system is a sensory system that has evolved over millions of years to detect acceleration of the head, both rotational and translational, in three dimensions. One of its most important functions is to stabilize gaze during unexpected head movement; however, it is also important in the control of posture and autonomic reflexes. Theta rhythm is a 3- to 12-Hz oscillating EEG signal that is intimately linked to self-motion and is also known to be important in learning and memory. Many studies over the last two decades have shown that selective activation of the vestibular system, using either natural rotational or translational stimulation, or electrical stimulation of the peripheral vestibular system, can induce and modulate theta activity. Furthermore, inactivation of the vestibular system has been shown to significantly reduce theta in freely moving animals, which may be linked to its impairment of place cell function as well as spatial learning and memory. The pathways through which vestibular information modulate theta rhythm remain debatable. However, vestibular responses have been found in the pedunculopontine tegmental nucleus (PPTg) and activation of the vestibular system causes an increase in acetylcholine release into the hippocampus, probably from the medial septum. Therefore, a pathway from the vestibular nucleus complex and/or cerebellum to the PPTg, supramammillary nucleus, posterior hypothalamic nucleus, and septum to the hippocampus is likely. The modulation of theta by the vestibular system may have implications for vestibular effects on cognitive function and the contribution of vestibular impairment to the risk of dementia.

Gamma Synchronization Influences Map Formation Time in a Topological Model of Spatial Learning.

The mammalian hippocampus plays a crucial role in producing a cognitive map of space—an internalized representation of the animal’s environment. We have previously shown that it is possible to model this map formation using a topological framework, in which information about the environment is transmitted through the temporal organization of neuronal spiking activity, particularly those occasions in which the firing of different place cells overlaps. In this paper, we discuss how gamma rhythm, one of the main components of the extracellular electrical field potential affects the efficiency of place cell map formation. Using methods of algebraic topology and the maximal entropy principle, we demonstrate that gamma modulation synchronizes the spiking of dynamical cell assemblies, which enables learning a spatial map at faster timescales.

Topological Gaussian ARAM for biologically inspired topological map building

This paper presents a new neural network for online topological map building inspired by beta oscillations and hippocampal place cell learning. The memory layer represents the hippocampus, the input layer represents the entorhinal, and the $$\rho$$ is the orientation system. In this model, multiple-scale entorhinal grid cell activations form the input layer feature patterns, which are categorized by hippocampal place cells (nodes) and act as spatial categories in the memory layer. Top-down attentive matching and mismatch-mediated reset (beta oscillations), which are triggered by the orientation system, overcome the stability-plasticity dilemma and prevent the catastrophic forgetting of place cell maps. In our proposed method, nodes in the topological map represent place cells (robot location), while edges connect nodes and store robot action (i.e., orientation, direction). Our method is based upon a multi-channel Adaptive Resonance Associative Memory (ARAM) network architecture to obtain multiple sensory sources for topological map building. It comprises two layers: input and memory. The input layer collects sensory data and incrementally clusters the obtained information into a set of topological nodes. In the memory layer, the clustered information is used as a topological map where nodes are associated with actions. The advantages of the proposed method are: (1) it does not require high-level cognitive processes and prior knowledge to make it work in a natural environment; and (2) it can process multiple sensory sources simultaneously in continuous space, which is crucial for real-world robot navigation. Thus, we combine our Topological Gaussian ARAM method (TGARAM) with incremental principle component analysis to constitute a basis for topological map building. Lastly, the proposed method was validated using several standardized benchmark datasets.

3-D Maps and Compasses in the Brain.

The world has a complex, three-dimensional (3-D) spatial structure, but until recently the neural representation of space was studied primarily in planar horizontal environments. Here we review the emerging literature on allocentric spatial representations in 3-D and discuss the relations between 3-D spatial perception and the underlying neural codes. We suggest that the statistics of movements through space determine the topology and the dimensionality of the neural representation, across species and different behavioral modes. We argue that hippocampal place-cell maps are metric in all three dimensions, and might be composed of 2-D and 3-D fragments that are stitched together into a global 3-D metric representation via the 3-D head-direction cells. Finally, we propose that the hippocampal formation might implement a neural analogue of a Kalman filter, a standard engineering algorithm used for 3-D navigation.

Is there a pilot in the brain? Contribution of the self-positioning system to spatial navigation

Since the discovery of place cells, the hippocampus is thought to be the neural substrate of a cognitive map. The later discovery of head direction cells, grid cells and border cells, as well as of cells with more complex spatial signals, has led to the idea that there is a brain system devoted to providing the animal with the information required to achieve efficient navigation. Current questioning is focused on how these signals are integrated in the brain. In this review, we focus on the issue of how self-localization is performed in the hippocampal place cell map. To do so, we first shortly review the sensory information used by place cells and then explain how this sensory information can lead to two coding modes, respectively based on external landmarks (allothetic information) and self-motion cues (idiothetic information). We hypothesize that these two modes can be used concomitantly with the rat shifting from one mode to the other during its spatial displacements. We then speculate that sequential reactivation of place cells could participate in the resetting of self-localization under specific circumstances and in learning a new environment. Finally, we provide some predictions aimed at testing specific aspects of the proposed ideas.

Virtual reality changes neuronal firing

Neuroscience Place cells are a type of neuron in the brain that gives you a sense of place and allows for spatial navigation. Visual cues are important for determining place cell function, but do other sensory or motor cues also play a role? To find out, Aghajan et al. compared rats navigating mazes in the real world and rats doing the same thing in virtual reality, where the only input they received was from distal visual cues. By monitoring the firing of place cells, the authors found that these cells did not perform optimally in the rats experiencing the virtual reality mazes. These results suggest that place cells need more than just visual signals. Nat. Neurosci. 10.1038/nn.3884 (2014).

A hierarchical model of goal directed navigation selects trajectories in a visual environment

We have developed a Hierarchical Look-Ahead Trajectory Model (HiLAM) that incorporates the firing pattern of medial entorhinal grid cells in a planning circuit that includes interactions with hippocampus and prefrontal cortex. We show the model’s flexibility in representing large real world environments using odometry information obtained from challenging video sequences. We acquire the visual data from a camera mounted on a small tele-operated vehicle. The camera has a panoramic field of view with its focal point approximately 5cm above the ground level, similar to what would be expected from a rat’s point of view. Using established algorithms for calculating perceptual speed from the apparent rate of visual change over time, we generate raw dead reckoning information which loses spatial fidelity over time due to error accumulation. We rectify the loss of fidelity by exploiting the loop-closure detection ability of a biologically inspired, robot navigation model termed RatSLAM. The rectified motion information serves as a velocity input to the HiLAM to encode the environment in the form of grid cell and place cell maps. Finally, we show goal directed path planning results of HiLAM in two different environments, an indoor square maze used in rodent experiments and an outdoor arena more than two orders of magnitude larger than the indoor maze. Together these results bridge for the first time the gap between higher fidelity bio-inspired navigation models (HiLAM) and more abstracted but highly functional bio-inspired robotic mapping systems (RatSLAM), and move from simulated environments into real-world studies in rodent-sized arenas and beyond.

A biologically inspired hierarchical goal directed navigation model

We propose an extended version of our previous goal directed navigation model based on forward planning of trajectories in a network of head direction cells, persistent spiking cells, grid cells, and place cells. In our original work the animat incrementally creates a place cell map by random exploration of a novel environment. After the exploration phase, the animat decides on its next movement direction towards a goal by probing linear look-ahead trajectories in several candidate directions while stationary and picking the one activating place cells representing the goal location. In this work we present several improvements over our previous model. We improve the range of linear look-ahead probes significantly by imposing a hierarchical structure on the place cell map consistent with the experimental findings of differences in the firing field size and spacing of grid cells recorded at different positions along the dorsal to ventral axis of entorhinal cortex. The new model represents the environment at different scales by populations of simulated hippocampal place cells with different firing field sizes. Among other advantages this model allows simultaneous constant duration linear look-ahead probes at different scales while significantly extending each probe range. The extension of the linear look-ahead probe range while keeping its duration constant also limits the degrading effects of noise accumulation in the network. We show the extended model’s performance using an animat in a large open field environment.

NMDA Receptors Control Cue-Outcome Selectivity and Plasticity of Orbitofrontal Firing Patterns during Associative Stimulus-Reward Learning

Neural activity in orbitofrontal cortex has been linked to flexible representations of stimulus-outcome associations. Such value representations are known to emerge with learning, but the neural mechanisms supporting this phenomenon are not well understood. Here, we provide evidence for a causal role for NMDA receptors (NMDARs) in mediating spike pattern discriminability, neural plasticity, and rhythmic synchronization in relation to evaluative stimulus processing and decision making. Using tetrodes, single-unit spike trains and local field potentials were recorded during local, unilateral perfusion of an NMDAR blocker in rat OFC. In the absence of behavioral effects, NMDAR blockade severely hampered outcome-selective spike pattern formation to olfactory cues, relative to control perfusions. Moreover, NMDAR blockade shifted local rhythmicsynchronization to higher frequencies and degradedits linkage to stimulus-outcome selective coding.These results demonstrate the importance of NMDARs for cue-outcome associative coding in OFC during learning and illustrate how NMDAR blockade disrupts network dynamics.

Increased Size and Stability of CA1 and CA3 Place Fields in HCN1 Knockout Mice

Hippocampal CA1 and CA3 pyramidal neuron place cells encode the spatial location of an animal through localized firing patterns called "place fields." To explore the mechanisms that control place cell firing and their relationship to spatial memory, we studied mice with enhanced spatial memory resulting from forebrain-specific knockout of the HCN1 hyperpolarization-activated cation channel. HCN1 is strongly expressed in CA1 neurons and in entorhinal cortex grid cells, which provide spatial information to the hippocampus. Both CA1 and CA3 place fields were larger but more stable in the knockout mice, with the effect greater in CA1 than CA3. As HCN1 is only weakly expressed in CA3 place cells, their altered activity likely reflects loss of HCN1 in grid cells. The more pronounced changes in CA1 likely reflect the intrinsic contribution of HCN1. The enhanced place field stability may underlie the effect of HCN1 deletion to facilitate spatial learning and memory.

Theta-paced flickering between place-cell maps in the hippocampus

The ability to recall discrete memories is thought to depend on the formation of attractor states in recurrent neural networks. In such networks, representations can be reactivated reliably from subsets of the cues that were present when the memory was encoded, at the same time as interference from competing representations is minimized. Theoretical studies have pointed to the recurrent CA3 system of the hippocampus as a possible attractor network. Consistent with predictions from these studies, experiments have shown that place representations in CA3 and downstream CA1 tolerate small changes in the configuration of the environment but switch to uncorrelated representations when dissimilarities become larger. However, the kinetics supporting such network transitions, at the subsecond timescale, is poorly understood. Here we show in rats that instantaneous transformation of the spatial context does not change the hippocampal representation all at once but is followed by temporary bistability in the discharge activity of CA3 ensembles. Rather than sliding through a continuum of intermediate activity states, the CA3 network undergoes a short period of competitive flickering between preformed representations of the past and present environment before settling on the latter. Network flickers are extremely fast, often with complete replacement of the active ensemble from one theta cycle to the next. Within individual cycles, segregation is stronger towards the end, when firing starts to decline, pointing to the theta cycle as a temporal unit for expression of attractor states in the hippocampus. Repetition of pattern-completion processes across successive theta cycles may facilitate error correction and enhance discriminative power in the presence of weak and ambiguous input cues.

Seeing into the future

The resting brain recapitulates activity patterns that occurred during a recent experience, possibly to aid long-term memory formation. Surprisingly, corresponding brain activity also occurs before an event happens. See Letter p.397 Place cells in the hippocampus track an animal's position in its environment. Previous work contends that sequential place-cell maps are produced during the first visit to a new area, and later consolidated at rest or during sleep. George Dragoi and Susumu Tonegawa report that place-cell firing patterns occur during rest or sleep before a novel spatial experience. They call this 'preplay', and because these sequences are distinct from the replay of previous experience, they suggest that it serves to prepare cell assemblies for any novel encoding that may occur in the near future.

Hippocampal learning and cognitive maps as products of hierarchical latent variable models

Event Abstract Back to Event Hippocampal learning and cognitive maps as products of hierarchical latent variable models Adam Johnson1*, Zachary Varberg1 and Paul Schrater2 1 Bethel University , United States 2 University of Minnesota, United States The hippocampus supports inference within early learning regimes and on a variety of complex spatial/context memory tasks. The defining characteristic of hippocampus dependent learning is that it provides the basis for a broad class of inferences in domains where sampling is limited relative to the potential dimensionality of inputs. Standard approaches to spatial learning and context learning have used neural network models to describe generalization behavior at the level of place cell maps and at the level of animal behavior. However, these models often overfit training data and compromise inference for novel test data. And in many cases, pattern classification dynamics observed in neural network models can be easily accommodated within probabilistic inference frameworks. We develop a model-based Bayesian approach to learning hierarchical latent variable structure and dynamics for spatial and context learning tasks. This approach expands probabilistic treatments of classical conditioning (Courville et al., 2003) to spatial learning domains for inference related to context dependent learning rules (e.g. "odor/reward position" paired-associate learning). These context dependent learning rules can be found by computing the posterior probabilities over latent variable hyperparameters. As a consequence, resultant behavioral inferences for novel test items reflect a mixture of latent models and are generally more robust. We model the spatial paired-associate task developed by Morris and colleagues (Day, Langston and Morris, 2003; Tse et al., 2007). Tse et al. (2007) showed that following initial training, rats learn new paired associates in a single trial. Single trial learning was dependent on the hippocampus and the consistency of the previously learned paired-associates within a given context. We show that initial training tunes the hyperparameter posterior density and consequently facilitates learning subsequent novel paired-associates - both in the search for newly learned paired-associate food-sites and for the avoidance of known food-sites following presentation of a novel odor. Furthermore, we show that the structure of the task requires the use of a full conjunctive representation for learning new paired-associates but does not require a full conjunctive representation for retrieval of previously learned paired-associates. Together these results show how the hippocampus facilitates new learning but is not always necessary for retrieval of previously learned information. Morris and colleagues suggest the hippocampus supports schema representations that facilitate new learning within a context. While this perspective has provided important insights into animal learning theory, it has lacked a computational counterpart. We suggest that hippocampus dependent schematic representations and cognitive maps are the consequence of a process that probabilistically infers hierarchical latent structure from input data. This statistical approach supports model optimization for inference across test data rather than model optimization across training data. As a result, the latent variable approach provides a much more robust inference engine than standard neural network models. Finally, the extraction of latent structure from input data allows for a simple explanation of hippocampal function and its important role within early learning regimes. Conference: Computational and Systems Neuroscience 2010, Salt Lake City, UT, United States, 25 Feb - 2 Mar, 2010. Presentation Type: Poster Presentation Topic: Poster session II Citation: Johnson A, Varberg Z and Schrater P (2010). Hippocampal learning and cognitive maps as products of hierarchical latent variable models. Front. Neurosci. Conference Abstract: Computational and Systems Neuroscience 2010. doi: 10.3389/conf.fnins.2010.03.00213 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 04 Mar 2010; Published Online: 04 Mar 2010. * Correspondence: Adam Johnson, Bethel University, St. Paul, United States, adam-johnson@bethel.edu Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. 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