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Across the cortical hierarchy, single neurons are characterized by differences in the extent to which they can sustain their firing rate over time (i.e., their "intrinsic timescale"). Previous studies have demonstrated that neurons in a given brain region mostly exhibit either short or long intrinsic timescales. In this study, we sought to identify populations of neurons that accumulate information over different timescales in the mouse brain and to characterize their functions in the context of a visual discrimination task. Thus, we separately examined the neural population dynamics of neurons with long or short intrinsic timescales across different brain regions. More specifically, we looked at the decoding performance of these neural populations aligned to different task variables (stimulus onset, movement). Taken together, our population-level findings support the hypothesis that long intrinsic timescale neurons encode abstract variables related to decision formation. Furthermore, we investigated whether there was a relationship between how well a single neuron represents the animal's choice or stimuli and their intrinsic timescale. We did not observe any significant relationship between the decoding of these task variables and a single neuron's intrinsic timescale. In summary, our findings support the idea that the long intrinsic timescale population of neurons, which appear at different levels of the cortical hierarchy, are primarily more involved in representing the decision variable.

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Perceptual decision making, as a process of detecting and categorizing information, has been studied extensively over the last two decades. In this study, we investigated the neural characterization of the whole decision-making process by discovering the information processing stages. Such that, the timing and the neural signature of the processing stages were identified for individual trials. The association of stages duration with the stimulus coherency and spatial prioritization factors also revealed the importance of the evidence accumulation process on the speed of the whole decisionmaking process. We reported that the impact of the stimulus coherency and spatial prioritization on the neural representation of the decision-making process was consistent with the behavioral characterization as well. This study demonstrated that uncovering the cognitive processing stages provided more insights into the decision-making process.Keywords: perceptual decision making, neural and behavioral characterizations, cognitive processing stages a b

Perceptual decision making, as a process of detecting and categorizing information, has been studied extensively over the last two decades. In this study, we investigated the neural characterization of the whole decision-making process by discovering the information processing stages. Such that, the timing and the neural signature of the processing stages were identified for individual trials. The association of stages duration with the stimulus coherency and spatial prioritization factors also revealed the importance of the evidence accumulation process on the speed of the whole decisionmaking process. We reported that the impact of the stimulus coherency and spatial prioritization on the neural representation of the decision-making process was consistent with the behavioral characterization as well. This study demonstrated that uncovering the cognitive processing stages provided more insights into the decision-making process.

The gradual accumulation of noisy evidence for or against options is the main step in the perceptual decision-making process. Using brain-wide electrophysiological recording in mice (Steinmetz et al., 2019), we examined neural correlates of evidence accumulation across multiple brain areas. We demonstrated that the neurons across the brain exhibited ramping-like firing rate activity that was modulated by the strength of evidence. These neurons had distinct properties in their intrinsic timescale, which were organized hierarchically across the brain. Our findings support the existence of evidence accumulation over multiple timescales. Besides variability across brain regions, a heterogeneity of intrinsic timescales was observed within each brain region as well. We demonstrated that this variability reflected the heterogeneity of microcircuit accumulation parameters, such that populations with longer timescales had higher recurrent excitation strength.