P3

100ms

Offset

Temporal Discrimination Task Simple Reaction Control Task

FIGURE 13.3 Grand average ERPs elicited by the auditory stimuli at the onset and offset (n = 9). Temporal discrimination task corrected by 2- and 8-sec auditory stimuli. Simple reaction time task corrected by only 2-sec auditory stimulus. Negative is up.

Sampling Phase

The ERPs consisted of N1, P2, and P3 components. In the onset ERPs, P3 components were consistently observed for the frontal cortex, hippocampal CA1, and cerebellar cortex in the T-task. The P2 component showed a significant main effect of amplitude that was larger for the T-task than for the C-task. For all statistical tests, the significance level was set at .05. A significant main effect of region for the P2 component latency was obtained with analysis of variance (ANOVA) in three brain regions across the two tasks. Post hoc tests revealed that the P2 component latency was longer for the frontal cortex than for the hippocampus and the cerebellum.

13.2.2 Correlation between EEG and Learning

It is well known that there is a strong correlation between EEG frequency and various sleep-wake states. EEG frequency is one of the indices used to measure states of arousal. The pattern of electrical activity in a fully awake animal is a mixture of many frequencies dominated by waves of relatively fast frequencies and low amplitude. The EEG will change to low frequencies and high amplitude depending on the stage of sleep (Datta and Hobson, 2000). Visual evoked potentials (VEPs) in the rat showed the same pattern of state-dependent changes in amplitude (Meeren et al., 1998). On the other hand, there have been relatively few studies of cognitive activity based on the analysis of the ongoing EEG. Sato and Sakata (1999) recorded EEG from the rat during the performance of a delayed nonmatching-to-sample task. Between the hippocampal CA1 and the entorhinal cortex, and the CA1 and the cingulated cortex, EEG synchronization was higher in three task-related phases (sample, delay, and comparison) than in the control phase. There are few data showing a clear correlation between the EEG and learning processes in spite of the fact that many investigators believe this correlation to be present. It is also unclear what the relationship is between neural firing and EEG as a field potential. In the analysis of neural firing associated with learning, it is important to take into account the ongoing background EEG as a reflection of the current state.

13.2.3 Implications of Electrophysiological Activity

The temporal and spatial information provided by EEG may be used to understand how the brain implements behavioral change, feeling, and memory. Electrophysio-logical activity is the origin of behavior or processing by the brain. We can measure the EEG when animals are performing specific tasks. What does the EEG reflect, input, or output? Many investigators have tended to assume the origin of EEG activity is the mind, the so-called "source of controlled, observable variability" (e.g., Picton et al., 2000). How about a timing event? "Time flies when you're having fun," is an often-quipped adage that demonstrates our sensitivity to the time course of events in our everyday lives (Matell and Meck, 2000). How could we explain interval timing by neural mechanisms? Mattel and Meck (2000) proposed a realtime neuropsychological model, the striatal beat frequency (SBF) model, which consists of oscillating neurons. The SBF model serves as a bridge between the firing neurons and field potentials. The point of the SBF model is to identify the clock process of the information-processing model of interval timing. Nevertheless, it is still difficult to envision to what extent timing plays a role in behavior without additional neural indices.

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