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Processing of time intervals in short tem-poral ranges, typically in sub- and supra-second intervals, is vital for the survivalof primates and humans. Research evi-dence has already shown that the process-ing of sub- and supra-second intervals issubserved by different neural mechanisms(Buhusi and Meck, 2005). In the domainof time perception, one outstanding sci-entic question remains to be solved iswhether the interval processing is imple-mented by a central clock (supra-sensory)or specic clock (neural state-specic)representation (Ivry and Schlerf, 2008).Gupta (2014) initiated theoretical explo-rations by introducing different types ofneuronal oscillators, with three key com-ponents:pacemakerneurons,tonicinputs,and synchronized excitation/inhibition ofinter-connected neurons (Gupta, 2014).Gupta further featured three characteris-ticfactorsinanintegrated model:(1).Theencoding of the time interval is reectedby frequency modulated neuronal spikesor spikebursts afliated with thetemporalpoints of a given interval. (2). Our brainembraces active calibration mechanismsamong multiple oscillators and modularconnections, which could to a large extentpitagainstthelossofintervaltimingfunc-tions of the brain. (3). The neuronal oscil-lators have solid foundations in neuralsubstrates that typically characterize sen-sorimotor timing, from the changes ofmembrane potential to the functioningof high-level brain structures (Teki et al.,2011; Santos et al., 2014).The concept of the building block for“neural oscillators” in Gupta (2014) inessence could be extended and interpretedin different contexts: Firstly, the “neu-ronaltemporalunits”in, Gupta (2014)in contrast to the traditional “pacemaker-accumulator” model (Treisman, 1963;Treisman et al., 1990, 1994; Wittmann,1999), is a machinery device (inherent inthebrain)thatprocessesthetimeinforma-tion. Secondly, the “oscillators” entail the“modular” nature of neuronal clocks, byemphasizing the correspondence betweenneural substrates in temporal bindingsbetween different sensory events. In thisway and from a broad perspective, thismodel dovetails the tenets of “intrin-sic model” (Ivry and Schlerf, 2008), butallows mechanistic exibility for the inter-actions between separate clocks. Thirdly,the oscillators could demonstrate higher-level ensemble neuronal activities (butwere relatively less addressed in cur-rentmodel),includingoscillatorygamma-band responses (Herrmann et al., 2004;Tallon-Baudry and Bertrand, 1999).Movement is the expressed time (Tekiet al., 2011). The Theoretical modelfor Gupta (2014) stems from the com-mon setting of “movement control” butencourages more potential investigationstocome.Forexample,itcouldbeextendedand validated in other timing scenarios—go beyond the area of motor control—todemonstrate its robustness. Moreover, atypicalfunctionofhumantimingsystemisto predict the imminent events and hencemake efcient perceptual decision mak-ing. The current oscillation model leavesmuch room to be improved by revealinghowtheoscillatorscouldworkdirectlyinaframework of predictive coding. This pre-dictivecodingissomehowfreeoffeedbackprocesses as assumed in Gupta (2014).Furthermore, a high-level processing ofrhythm/periodicity-based upon the infor-mation of sub- or supra-second intervals,might mobilizepopulation/ensemble neu-ronal coding as well as impose more com-plexinter-connections/overlappings ofthesupposed neuronal temporal units (withmultiple neuronal feedback loops). WhatGupta (2014) proposed essentially indi-cated a potential panorama of neuraloscillators that could address the com-plexity of timing behavior. However,how many (levels of) oscillators areneeded to elucidate those timing behav-iors and how local lateral excitatory con-nectivity (Gavornik and Shouval, 2011),interacting with the global (stochastic)processingofbeattiming,warrantsfurtherexplorations.

How many neural oscillators we need on sub- and supra-second intervals processing in the primate brain