Reward revaluation biases hippocampal replay content away from the preferred outcome.

The rodent hippocampus spontaneously generates bursts of neural activity (replay) that can depict spatial trajectories to reward locations, suggesting a role in model-based behavioral control. A largely separate literature emphasizes reward revaluation as the litmus test for such control, yet the content of hippocampal replay under revaluation conditions is unknown. We examined the content of awake replay events following motivational shifts between hunger and thirst. On a T-maze offering free choice between food and water outcomes, rats shifted their behavior toward the restricted outcome, but replay content was shifted away from the restricted outcome. This effect preceded experience on the task each day and did not reverse with experience. These results demonstrate that replay content is not limited to reflecting recent experience or trajectories toward the preferred goal and suggest a role for motivational states in determining replay content.

Optimizing for generalization in the decoding of internally generated activity in the hippocampus.

The decoding of a sensory or motor variable from neural activity benefits from a known ground truth against which decoding performance can be compared. In contrast, the decoding of covert, cognitive neural activity, such as occurs in memory recall or planning, typically cannot be compared to a known ground truth. As a result, it is unclear how decoders of such internally generated activity should be configured in practice. We suggest that if the true code for covert activity is unknown, decoders should be optimized for generalization performance using cross-validation. Using ensemble recording data from hippocampal place cells, we show that this cross-validation approach results in different decoding error, different optimal decoding parameters, and different distributions of error across the decoded variable space. In addition, we show that a minor modification to the commonly used Bayesian decoding procedure, which enables the use of spike density functions, results in substantially lower decoding errors. These results have implications for the interpretation of covert neural activity, and suggest easy-to-implement changes to commonly used procedures across domains, with applications to hippocampal place cells in particular. © 2017 Wiley Periodicals, Inc.

EEG analysis of frontal lobe area in arousal maintenance state against sleepiness.

This paper describes EEG analysis of frontal lobe area in arousal maintenance state against sleepiness. Arousal maintenance state is considered different physiological state from the normal sleep onset. To analyze the EEG of frontal area might be important because we believe that the arousal maintenance state against sleepiness causes neuron activities from the frontal lobe, which coordinates behavior, to hypothalamus, which coordinates wakefulness and sleep. It is, however, hard to use EEG signals in the frontal area consistently because blinking artifacts are mixed in the EEG signals. In this paper, we have analyzed the EEG signals of the frontal lobe in arousal maintenance state against sleepiness after removing the eye-blinking artifact from the scalp EEG signals using an ICA denoising method. As a result, the EEG signals of the frontal area in the arousal maintenance state against sleepiness have wide bandwidth as in the EEG of the occipital area. It strengthens our speculation, i.e., the EEG desynchronization occurs because of the neuron activities from the frontal lobe to hypothalamus in order to maintain arousal state against sleepiness.