No abstract

Humans use temporal regularities in their daily life to act in accordance with future events in the most efficient way. To achieve this, humans build temporal expectations and determine a template action that is in line with those expectations. In this temporal trisection study, we aimed to study the neurophysiological counterparts of temporal expectation and response discrimination. We investigated amplitude variations of early event-related potentials (ERPs) while manipulating time intervals.We measured temporal expectation-related attenuation of neural activity and response discrimination processes in N1 and P2 ERP components. Results showed that the amplitude of the N1 component was attenuated for the predicted task-relevant temporal location of a response decision. The P2 amplitude, in contrast, was enhanced for a discriminated response in comparison to a template response. The present study supports a link between the different functional associations of the N1 and P2 components within the requirements of a timing task. N1-related amplitude modulation can determine a change in expectation level during timing. The amplitude regulation of the P2 component, in contrast, explains temporal discrimination in both expected and unexpected temporal locations. In addition to expectation-related modulation, our results suggest an additional regulation of the N1 amplitude that is linked to attention. The effect was observed in instances that included a prediction error of a task-relevant temporal location for a response decision. In conclusion, our study contributes to the growing neurocognitive literature on interval timing by capturing different aspects of a timing task; namely, N1-related expectation and P2-related response discrimination processes.

Although the neural markers of interval timing have been widely studied, the events that determine the onset and offset of an interval have only recently started to gain attention. In the present study, I compare the predictions of the perceptual (preonset and start-gun) and decisional bias hypotheses with respect to onset N1P2 amplitude, the point of subjective equality (PSE) and delta/theta activity. The onsets of the comparison intervals (CIs) were manipulated to begin earlier, later, or on-time with regard to a standard interval (SI). Results supported the start-gun account by demonstrating an increase in the N1P2 amplitude and delta power in the "early" and "late" onset conditions due to temporal mismatch. Delayed or premature initiation of timing with respect to the predicted temporal point were associated with rightward and leftward shifts in the PSEs of the "early" and "late" onset conditions, respectively. In addition to the observed increase in temporal prediction-related delta activity in the "early" and "late" onset conditions, higher theta power in the "early" onset suggested an additional neural response for unexpected events that might be linked to response caution. Moreover, the ramping activity during the CIs, namely the contingent negative variation (CNV), showed a decision-related attenuation toward the end of an interval in the "late" onset. The latter finding was supported by the changes in offset N1P2 amplitude. The present study contributes to the interval-timing literature by presenting support in favor of the hypothesis that the onset N1P2 is a neural marker for the initiation of timing.

Recent advances in neuroscience have underscored the role of single neurons in information processing. Much of this work has focused on the role of neurons' dendrites to perform complex local computations that form the basis for the global computation of the neuron. Generally, artificial neural networks that are capable of real-time simulation do not take into account the principles underlying single-neuron processing. In this paper we propose a design for a neural model executed on the graphics processing unit (GPU) that is capable of simulating large neural networks that utilize dendritic computation inspired by biological neurons. We subsequently test our design using a neural model of the retinal neurons that contribute to the activation of starburst amacrine cells, which, as in biological retinas, use dendritic computational abilities to produce a neural signal that is directionally selective to stimuli moving centrifugally.