Stimulus repetition produces a decrease of the response and affects neuronal synchronization of macaque inferior
temporal (IT) neurons. Previously we showed that such stimulus-specific adaptation results in a decreased accuracy
by which IT neurons encode repeated compared to non-repeated objects. Not only spiking activity, but also local field
potentials (LFPs) are affected by repetition. Here we ask how the repetition-induced changes in IT LFPs affect object
decoding accuracy. To answer this, we recorded local field potentials using a laminar microelectrode in macaque IT.
We presented two familiar stimuli each for 500 ms successively with an inter-stimulus interval of 500 ms. Trials
consisted either of a repetition of the same stimulus or of their alternation. Machine learning-based classifier was
employed to decode stimulus identity from the LFP power in different frequency bands of each penetration. We found
that the object classification accuracy depended strongly on spectral frequency, with frequencies below 30 Hz (alpha
and beta) producing greater accuracies than gamma bands. However, the effect of repetition on classification
accuracy was stronger at the gamma frequencies, showing a decrease in classification accuracy for repeated stimuli
and a tendency for an improved object encoding when the stimulus was preceded by a different stimulus. The
present results demonstrate that due to adapting input, stimulus encoding in IT (1) can be more accurate for stimuli
that differ from recently preceding ones while being impaired for stimuli that are repeated, and (2) these effects are
more pronounced at high spectral frequencies of the LFP.