Department of Computer Engineering, Ferdowsi University of Mashhad, Mashhad, 9177948974, Iran.
Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139.
eNeuro. 2023 Nov 3;10(11). doi: 10.1523/ENEURO.0282-23.2023. Print 2023 Nov.
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 brain areas. We demonstrated that the neurons with drift-diffusion model (DDM)-like firing rate activity (i.e., evidence-sensitive ramping firing rate) were distributed across the brain. Exploring the underlying neural mechanism of evidence accumulation for the DDM-like neurons revealed different accumulation mechanisms (i.e., single and race) both within and across the brain areas. Our findings support the hypothesis that evidence accumulation is happening through multiple integration mechanisms in the brain. We further explored the timescale of the integration process in the single and race accumulator models. The results demonstrated that the accumulator microcircuits within each brain area had distinct properties in terms of their integration timescale, which were organized hierarchically across the brain. These findings support the existence of evidence accumulation over multiple timescales. Besides the variability of integration timescale across the brain, a heterogeneity of timescales was observed within each brain area as well. We demonstrated that this variability reflected the diversity of microcircuit parameters, such that accumulators with longer integration timescales had higher recurrent excitation strength.
随着支持或反对选项的证据逐渐积累,这是知觉决策过程中的主要步骤。我们使用小鼠全脑电生理记录(Steinmetz 等人,2019 年),研究了跨脑区的证据积累的神经相关性。我们证明,具有漂移扩散模型(DDM)样放电率活动的神经元(即,对证据敏感的渐变放电率)分布在整个大脑中。探索 DDM 样神经元证据积累的潜在神经机制,揭示了在脑区内部和之间存在不同的积累机制(即,单一和种族)。我们的研究结果支持这样一种假设,即证据积累是通过大脑中的多个整合机制发生的。我们进一步探索了在单一和种族积累器模型中整合过程的时间尺度。结果表明,每个脑区的积累器微电路在其整合时间尺度方面具有独特的性质,这些性质在大脑中呈层次组织。这些发现支持在多个时间尺度上进行证据积累的存在。除了跨脑的整合时间尺度的可变性外,在每个脑区内部也观察到了时间尺度的异质性。我们证明,这种可变性反映了微电路参数的多样性,即具有较长整合时间尺度的积累器具有更高的递归兴奋强度。