Corticosterone impairs flexible adjustment of spatial navigation in an associative place-reward learning task.

Cognitive challenges are often accompanied by a discharge of stress hormones, which in turn modulate multiple brain areas. Among these, the medial temporal lobe and the prefrontal cortex are critically involved in high-order cognitive functions such as learning, memory, and decision-making. Previous studies assessing the effects of corticosterone on spatial memory found an increase or a decrease in performance depending on the timing of stress hormone discharge relative to the behavioral task. Most of these studies, however, made use of aversively motivated behaviors, whereas less is known about corticosteroid effects on flexible learning during reward-driven spatial navigation. To study how corticosterone modulates flexible spatial learning, we tested rats on a place-reward association task where hormone treatment was administered immediately after a session presenting a change in reward locations. The corticosterone-treated group showed delayed learning during the initial sessions and suboptimal memory consolidation throughout testing. Repeated training on the novel reward positions improved performance and eliminated differences from the control group. We conclude that a marked increase in plasma corticosterone levels immediately after training impairs the flexible formation of new place-reward associations.

Reward Expectancy Strengthens CA1 Theta and Beta Band Synchronization and Hippocampal-Ventral Striatal Coupling.

The use of information from the hippocampal memory system in motivated behavior depends on its communication with the ventral striatum. When an animal encounters cues that signal subsequent reward, its reward expectancy is raised. It is unknown, however, how this process affects hippocampal dynamics and their influence on target structures, such as ventral striatum. We show that, in rats, reward-predictive cues result in enhanced hippocampal theta and beta band rhythmic activity during subsequent action, compared with uncued goal-directed navigation. The beta band component, also labeled theta's harmonic, involves selective hippocampal CA1 cell groups showing frequency doubling of firing periodicity relative to theta rhythmicity and it partitions the theta cycle into segments showing clear versus poor spike timing organization. We found that theta phase precession occurred over a wider range than previously reported. This was apparent from spikes emitted near the peak of the theta cycle exhibiting large "phase precessing jumps" relative to spikes in foregoing cycles. Neither this phenomenon nor the regular manifestation of theta phase precession was affected by reward expectancy. Ventral striatal neuronal firing phase-locked not only to hippocampal theta, but also to beta band activity. Both hippocampus and ventral striatum showed increased synchronization between neuronal firing and local field potential activity during cued compared with uncued goal approaches. These results suggest that cue-triggered reward expectancy intensifies hippocampal output to target structures, such as the ventral striatum, by which the hippocampus may gain prioritized access to systems modulating motivated behaviors. SIGNIFICANCE STATEMENT: Here we show that temporally discrete cues raising reward expectancy enhance both theta and beta band activity in the hippocampus once goal-directed navigation has been initiated. These rhythmic activities are associated with increased synchronization of neuronal firing patterns in the hippocampus and the connected ventral striatum. When transmitted to downstream target structures, this expectancy-related state of intensified processing in the hippocampus may modulate goal-directed action.

Reward cues in space: commonalities and differences in neural coding by hippocampal and ventral striatal ensembles.

Forming place-reward associations critically depends on the integrity of the hippocampal-ventral striatal system. The ventral striatum (VS) receives a strong hippocampal input conveying spatial-contextual information, but it is unclear how this structure integrates this information to invigorate reward-directed behavior. Neuronal ensembles in rat hippocampus (HC) and VS were simultaneously recorded during a conditioning task in which navigation depended on path integration. In contrast to HC, ventral striatal neurons showed low spatial selectivity, but rather coded behavioral task phases toward reaching goal sites. Outcome-predicting cues induced a remapping of firing patterns in the HC, consistent with its role in episodic memory. VS remapped in conjunction with the HC, indicating that remapping can take place in multiple brain regions engaged in the same task. Subsets of ventral striatal neurons showed a "flip" from high activity when cue lights were illuminated to low activity in intertrial intervals, or vice versa. The cues induced an increase in spatial information transmission and sparsity in both structures. These effects were paralleled by an enhanced temporal specificity of ensemble coding and a more accurate reconstruction of the animal's position from population firing patterns. Altogether, the results reveal strong differences in spatial processing between hippocampal area CA1 and VS, but indicate similarities in how discrete cues impact on this processing.

Multiplexing of Information about Self and Others in Hippocampal Ensembles.

In addition to coding a subject's location in space, the hippocampus has been suggested to code social information, including the spatial position of conspecifics. "Social place cells" have been reported for tasks in which an observer mimics the behavior of a demonstrator. We examine whether rat hippocampal neurons may encode the behavior of a minirobot, but without requiring the animal to mimic it. Rather than finding social place cells, we observe that robot behavioral patterns modulate place fields coding animal position. This modulation may be confounded by correlations between robot movement and changes in the animal's position. Although rat position indeed significantly predicts robot behavior, we find that hippocampal ensembles code additional information about robot movement patterns. Fast-spiking interneurons are particularly informative about robot position and global behavior. In conclusion, when the animal's own behavior is conditional on external agents, the hippocampus multiplexes information about self and others.

Hippocampal sharp waves and reactivation during awake states depend on repeated sequential experience.

Hippocampal firing patterns during behavior are reactivated during rest and subsequent slow-wave sleep. These reactivations occur during transient local field potential (LFP) events, termed sharp waves. Theories of hippocampal processing suggest that sharp waves arise from strengthened plasticity, and that the strengthened plasticity depends on repeated cofiring of pyramidal cells. We tested these predictions by recording neural ensembles and LFPs from rats running tasks requiring different levels of behavioral repetition. The number of sharp waves emitted increased during sessions with more regular behaviors. Reactivation became more similar to behavioral firing patterns across the session. This enhanced reactivation also depended on the regularity of the behavior. Additional studies in CA3 and CA1 found that the number of sharp waves emitted also increased in CA3 recordings as well as CA1, but that the time courses were different between the two structures.

Perirhinal firing patterns are sustained across large spatial segments of the task environment.

Spatial navigation and memory depend on the neural coding of an organism's location. Fine-grained coding of location is thought to depend on the hippocampus. Likewise, animals benefit from knowledge parsing their environment into larger spatial segments, which are relevant for task performance. Here we investigate how such knowledge may be coded, and whether this occurs in structures in the temporal lobe, supplying cortical inputs to the hippocampus. We found that neurons in the perirhinal cortex of rats generate sustained firing patterns that discriminate large segments of the task environment. This contrasted to transient firing in hippocampus and sensory neocortex. These spatially extended patterns were not explained by task variables or temporally discrete sensory stimuli. Previously it has been suggested that the perirhinal cortex is part of a pathway processing object, but not spatial information. Our results indicate a greater complexity of neural coding than captured by this dichotomy.

Cell-Type and State-Dependent Synchronization among Rodent Somatosensory, Visual, Perirhinal Cortex, and Hippocampus CA1.

Beta and gamma rhythms have been hypothesized to be involved in global and local coordination of neuronal activity, respectively. Here, we investigated how cells in rodent area S1BF are entrained by rhythmic fluctuations at various frequencies within the local area and in connected areas, and how this depends on behavioral state and cell type. We performed simultaneous extracellular field and unit recordings in four connected areas of the freely moving rat (S1BF, V1M, perirhinal cortex, CA1). S1BF spiking activity was strongly entrained by both beta and gamma S1BF oscillations, which were associated with deactivations and activations, respectively. We identified multiple classes of fast spiking and excitatory cells in S1BF, which showed prominent differences in rhythmic entrainment and in the extent to which phase locking was modulated by behavioral state. Using an additional dataset acquired by whole-cell recordings in head-fixed mice, these cell classes could be compared with identified phenotypes showing gamma rhythmicity in their membrane potential. We next examined how S1BF cells were entrained by rhythmic fluctuations in connected brain areas. Gamma-synchronization was detected in all four areas, however we did not detect significant gamma coherence among these areas. Instead, we only found long-range coherence in the theta-beta range among these areas. In contrast to local S1BF synchronization, we found long-range S1BF-spike to CA1-LFP synchronization to be homogeneous across inhibitory and excitatory cell types. These findings suggest distinct, cell-type contributions of low and high-frequency synchronization to intra- and inter-areal neuronal interactions.

Nanowires precisely grown on the ends of microwire electrodes permit the recording of intracellular action potentials within deeper neural structures.

AIMS: Nanoelectrodes are an emerging biomedical technology that can be used to record intracellular membrane potentials from neurons while causing minimal damage during membrane penetration. Current nanoelectrode designs, however, have low aspect ratios or large substrates and thus are not suitable for recording from neurons deep within complex natural structures, such as brain slices. MATERIALS & METHODS: We describe a novel nanoelectrode design that uses nanowires grown on the ends of microwire recording electrodes similar to those frequently used in vivo. RESULTS & DISCUSSION: We demonstrate that these nanowires can record intracellular action potentials in a rat brain slice preparation and in isolated leech ganglia. CONCLUSION: Nanoelectrodes have the potential to revolutionize intracellular recording methods in complex neural tissues, to enable new multielectrode array technologies and, ultimately, to be used to record intracellular signals in vivo.

An inside look at hippocampal silent cells.

Hippocampal pyramidal cells can be divided into place cells, which fire action potentials when an animal is in specific locations, and silent cells, which are not spatially selective. In this issue of Neuron, Epsztein et al. find intracellular differences between place and silent cells by using whole-cell recordings in freely moving rats.

Detecting dynamical changes within a simulated neural ensemble using a measure of representational quality.

Technological advances allowing simultaneous recording of neuronal ensembles have led to many developments in our understanding of how the brain performs neural computations. One key technique for extracting information from neural populations has been population reconstruction. While reconstruction is a powerful tool, it only provides a value and gives no indication of the quality of the representation itself. In this paper, we present a mathematically and statistically justified measure for assessing the quality of a representation in a neuronal ensemble. Using a simulated neural network, we show that this measure can distinguish between system states and identify moments of dynamical change within the system. While the examples used in this paper all derive from a standard network model, the measure itself is very general. It requires only a representational space, measured tuning curves, and neural ensembles.

Using arm configuration to learn the effects of gyroscopes and other devices.

Previous studies have perturbed the association between motor commands and arm movements by applying forces to the arm during two-dimensional movements. These studies have revealed that, when the normal hand path is perturbed, subjects gradually adapt their motor commands to return to this path. The present study used the spin of a gyroscope to create a complex perturbation, as subjects reached to targets presented in three dimensions. Hand path did not change, but the whole-arm geometry ("arm configuration" in four dimensions) was altered. Over a series of several hundred reaches to various targets, subjects gradually returned the arm movement to its normal configuration. Furthermore, during the course of this learning, subjects used a strategy that involved manipulating arm posture. A similar strategy was observed when subjects made reaching movements with a rod attached to the upper arm to change its inertial characteristics. In both cases, the gradual return to the normal arm movement was accomplished without an increase in kinetic energy, suggesting that arm postures and movements (kinematics) and muscular forces (kinetics) may be mutually optimized. In contrast to previous studies, the present results highlight the role of arm configuration (rather than hand path) in learning and control.