Alzheimer's disease (AD) is an irreversible and highly progressive neurodegenerative disease. Clinically, patients with AD display impairments in episodic and spatial memory. However, the underlying neuronal dysfunctions that result in these impairments remain poorly understood. The hippocampus is crucial for spatial and episodic memory, and thus we tested the hypothesis that abnormal neuronal representations of space in the hippocampus contribute to memory deficits in AD. To test this hypothesis, we recorded spikes from place cells in hippocampal subfield CA1, together with corresponding rhythmic activity in local field potentials, in the 3xTg AD mouse model. We observed disturbances in place cell firing patterns, many of which were consistent with place cell disturbances reported in other rodent models of AD. We found place cell representations of space to be unstable in 3xTg mice compared to control mice. Furthermore, coordination of place cell firing by hippocampal rhythms was disrupted in 3xTg mice. Specifically, a smaller proportion of place cells from 3xTg mice were significantly phase-locked to theta and slow gamma rhythms, and the theta and slow gamma phases at which spikes occurred were also altered. Remarkably, these disturbances were observed at an age before detectable Aβ pathology had developed. Consistencies between these findings in 3xTg mice and previous findings from other AD models suggest that disturbances in place cell firing and hippocampal rhythms are related to AD rather than reflecting peculiarities inherent to a particular transgenic model. Thus, disturbed rhythmic organization of place cell activity may contribute to unstable spatial representations, and related spatial memory deficits, in AD. © 2017 Wiley Periodicals, Inc.

The rodent hippocampus and entorhinal cortex contain spatially-modulated cells that serve as the basis for spatial coding. Both medial entorhinal grid cells and hippocampal place cells have been shown to encode spatial information across multiple spatial scales that increase along the dorsoventral axis of these structures. Place cells near the dorsal pole possess small, stable, and spatially selective firing fields, while ventral cells have larger, less stable and less spatially selective firing fields. One possible explanation for these dorsoventral changes in place field properties is that they arise as a result of similar dorsoventral differences in the properties of the grid cell inputs to place cells. Here we test the alternative hypothesis that dorsoventral place field differences are due to higher amounts of non-spatial inputs to ventral hippocampal cells. We use a computational model of the entorhinal-hippocampal network to assess the relative contributions of grid scale and non-spatial inputs in determining place field size and stability. In addition, we assess the consequences of grid node firing rate heterogeneity on place field stability. Our results suggest that dorsoventral differences in place cell properties can be better explained by changes in the amount of non-spatial inputs, rather than by changes in the scale of grid cell inputs, and that grid node heterogeneity may have important functional consequences. The observed gradient in field size may reflect a shift from processing primarily spatial information in the dorsal hippocampus to processing more non-spatial, contextual and emotional information near the ventral hippocampus.

CA1 place cells become more anticipatory with experience, an effect thought to be caused by NMDA receptor-dependent plasticity in the CA3-CA1 network. Theta (~5-12 Hz), slow gamma (~25-50 Hz), and fast gamma (~50-100 Hz) rhythms are thought to route spatial information in the hippocampal formation and to coordinate place cell ensembles. Yet, it is unknown whether these rhythms exhibit experience-dependent changes concurrent with those observed in place cells. Slow gamma rhythms are thought to indicate inputs from CA3 to CA1, and such inputs are thought to be strengthened with experience. Thus, we hypothesized that slow gamma rhythms would become more evident with experience. We tested this hypothesis using mice freely traversing a familiar circular track for three 10-min sessions per day. We found that slow gamma amplitude was reduced in the early minutes of the first session of each day, even though both theta and fast gamma amplitudes were elevated during this same period. However, in the first minutes of the second and third sessions of each day, all three rhythms were elevated. Interestingly, theta was elevated to a greater degree in the first minutes of the first session than in the first minutes of later sessions. Additionally, all three rhythms were strongly influenced by running speed in dynamic ways, with the influence of running speed on theta and slow gamma changing over time within and across sessions. These results raise the possibility that experience-dependent changes in hippocampal rhythms relate to changes in place cell activity that emerge with experience. NEW & NOTEWORTHY We show that CA1 theta, slow gamma, and fast gamma rhythms exhibit characteristic changes over time within sessions in familiar environments. These effects in familiar environments evolve across repeated sessions.

For over 50 years, the satisfaction of search effect has been studied within the field of radiology. Defined as a decrease in detection rates for a subsequent target when an initial target is found within the image, these multiple target errors are known to underlie errors of omission (e.g., a radiologist is more likely to miss an abnormality if another abnormality is identified). More recently, they have also been found to underlie lab-based search errors in cognitive science experiments (e.g., an observer is more likely to miss a target ‘T’ if a different target ‘T’ was detected). This phenomenon was renamed the subsequent search miss (SSM) effect in cognitive science. Here we review the SSM literature in both radiology and cognitive science and discuss: (1) the current SSM theories (i.e., satisfaction, perceptual set, and resource depletion theories), (2) the eye movement errors that underlie the SSM effect, (3) the existing efforts tested to alleviate SSM errors, and (4) the evolution of methodologies and analyses used when calculating the SSM effect. Finally, we present the attentional template theory, a novel mechanistic explanation for SSM errors, which ties together our current understanding of SSM errors and the attentional template literature.

Rodent spatial navigation requires the dynamic evaluation of multiple sources of information, including visual cues, self-motion signals and reward signals. The nature of the evaluation, its dynamics and the relative weighting of the multiple information streams are largely unknown and have generated many hypotheses in the field of robotics. We use the framework of the traveling salesperson problem (TSP) to study how this evaluation may be achieved. The TSP is a classical artificial intelligence NP-hard problem that requires an agent to visit a fixed set of locations once, minimizing the total distance traveled. We show that after a few trials, rats converge on a short route between rewarded food cups. We propose that this route emerges from a series of local decisions that are derived from weighing information embedded in the context of the task. We study the relative weighting of spatial and reward information and establish that, in the conditions of this experiment, when the contingencies are not in conflict, rats choose the spatial or reward optimal solution. There was a trend toward a preference for space when the contingencies were in conflict. We also show that the spatial decision about which cup to go to next is biased by the orientation of the animal. Reward contingencies are also shown to significantly and dynamically modulate the decision-making process. This paradigm will allow for further neurophysiological studies aimed at understanding the synergistic role of brain areas involved in planning, reward processing and spatial navigation. These insights will in turn suggest new neural-like architectures for the control of mobile autonomous robots.