Repeated stimulus causes a specific suppression of neuronal responses, which is so-called as Stimulus-Specific Adaptation (SSA). This effect can be recovered when the stimulus changes. In the auditory system SSA is a well-known phenomenon that appears at different levels of the mammalian auditory pathway. In this study, we explored the effects of adaptation to a particular stimulus on the auditory tuning curves of anesthetized rats. We used two sequences and compared the responses of each tone combination in these two conditions. First sequence consists of different pure tone combinations that were presented randomly. In the second one, the same stimuli of the first sequence were presented in the context of an adapted stimulus (adapter) that occupied 80% of sequence probability. The population results demonstrated that the adaptation factor decreased the frequency response area and made a change in the tuning curve to shift it unevenly toward the higher thresholds of tones. The local field potentials and multi-unit activity responses have indicated that the neural activities strength of the adapted frequency has been suppressed as well as with lower suppression in neighboring frequencies. This aforementioned reduction changed the characteristic frequency of the tuning curve.

Stimulus repetition suppresses the neural activity in different sensory areas of the brain. This mechanism of so-called stimulus-specific adaptation (SSA) has been observed in both spiking activity and local field potential (LFP) responses. However, much remains to be known about the effect of SSA on the spike–LFP relation. In this study, we approached this issue by investigating the spike-phase coupling (SPC) in control and adapting paradigms. For the control paradigm, pure tones were presented in a random unbiased sequence. In the adapting paradigm, the same stimuli were presented in a random pattern but it was biased to an adapter stimulus. In fact, the adapter occupied 80% of the adapting sequence. During the tasks, LFP and multi-unit activity were recorded simultaneously from the primary auditory cortex of 15 anesthetized rats. To clarify the effect of adaptation on the relation between spike and LFP responses, the SPC of the adapter stimulus in these two paradigms was evaluated. Here, we employed phase locking value method for calculating the SPC. Our data show a strong coupling of spikes to LFP phase most prominently in beta band. This coupling was observed to decrease in the adapting condition compared to the control one. Importantly, we found that adaptation reduces spikes dominantly from the preferred phase of LFP in which spikes are more likely to be present there. As a result, the preferred phase of LFP may play a key role in coordinating neuronal spiking activity in neural adaptation mechanism. This finding is important for interpretation of the underlying neural mechanism of adaptation and also can be used in the context of the network and related connectivity models.

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How neural activity is linked to behavior is a critical question in neural engineering and cognitive neurosciences. It is crucial to predict behavior as early as possible, to plan a machine response in real-time brain computer interactions. However, previous studies have studied the neural readout of behavior only within a short time before the action is performed. This leaves unclear, if the neural activity long before a decision could predict the upcoming behavior. By recording extracellular neural activities from the visual cortex of behaving rhesus monkeys, we show that: (1) both, local field potentials (LFPs) and the rate of neural spikes long before (>2 s) a monkey responds to a change, foretell its behavioral performance in a spatially selective manner; (2) LFPs, the more accessible component of extracellular activity, are a stronger predictor of behavior; and (3) LFP amplitude is positively correlated while spiking activity is negatively correlated with behavioral reaction time (RT). These results suggest that field potentials could be used to predict behavior way before it is performed, an observation that could potentially be useful for brain computer interface applications, and that they contribute to the sensory neural circuit’s speed in information processing.

How does the human brain prioritize different visual representations in working memory (WM)? Here, we define the oscillatory mechanisms supporting selection of “where” and “when” features from visual WM storage and investigate the role of pFC in feature selection. Fourteen individuals with lateral pFC damage and 20 healthy controls performed a visuospatial WM task while EEG was recorded. On each trial, two shapes were presented sequentially in a top/bottom spatial orientation. A retro-cue presented mid-delay prompted which of the two shapes had been in either the top/bottom spatial position or first/second temporal position. We found that cross-frequency coupling between parieto-occipital alpha (α; 8–12 Hz) oscillations and topographically distributed gamma (γ; 30–50 Hz) activity tracked selection of the distinct cued feature in controls. This signature of feature selection was disrupted in patients with pFC lesions, despite intact α–γ coupling independent of feature selection. These findings reveal a pFC-dependent parieto-occipital α–γ mechanism for the rapid selection of visual WM representations.

Adaptation is an important mechanism that causes a decrease in the neural response both in terms of local field potentials (LFP) and spiking activity. We previously showed this reduction effect in the tuning curve of the primary auditory cortex. Moreover, we revealed that a repeated stimulus reduces the neural response in terms of spike-phase coupling (SPC). In the current study, we examined the effect of adaptation on the SPC tuning curve. To this end, employing the phase-locking value (PLV) method, we estimated the spike-LFP coupling. The data was obtained by a simultaneous recording from four single-electrodes in the primary auditory cortex of 15 rats. We first investigated whether the neural system may use spike-LFP phase coupling in the primary auditory cortex to encode sensory information. Secondly, we investigated the effect of adaptation on this potential SPC tuning. Our data showed that the coupling between spikes' times and the LFP phase in beta oscillations represents sensory information (different stimulus frequencies), with an inverted bell-shaped tuning curve. Furthermore, we showed that adaptation to a specific frequency modulates SPC tuning curve of the adapter and its neighboring frequencies. These findings could be useful for interpretation of feature representation in terms of SPC and the underlying neural mechanism of adaptation.

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