Computational Neurophysics Lab, Institute for Theoretical Physics, University of Bremen, Bremen, Germany.
Brain Research Institute, University of Bremen, Bremen, Germany.
PLoS Comput Biol. 2021 Nov 12;17(11):e1009595. doi: 10.1371/journal.pcbi.1009595. eCollection 2021 Nov.
Sudden changes in visual scenes often indicate important events for behavior. For their quick and reliable detection, the brain must be capable to process these changes as independently as possible from its current activation state. In motion-selective area MT, neurons respond to instantaneous speed changes with pronounced transients, often far exceeding the expected response as derived from their speed tuning profile. We here show that this complex, non-linear behavior emerges from the combined temporal dynamics of excitation and divisive inhibition, and provide a comprehensive mathematical analysis. A central prediction derived from this investigation is that attention increases the steepness of the transient response irrespective of the activation state prior to a stimulus change, and irrespective of the sign of the change (i.e. irrespective of whether the stimulus is accelerating or decelerating). Extracellular recordings of attention-dependent representation of both speed increments and decrements confirmed this prediction and suggest that improved change detection derives from basic computations in a canonical cortical circuitry.
视觉场景的突然变化通常表明行为的重要事件。为了快速可靠地检测到这些变化,大脑必须能够尽可能独立于其当前的激活状态来处理这些变化。在运动选择性区域 MT 中,神经元会对瞬间的速度变化做出明显的瞬态反应,其反应幅度常常远远超过根据其速度调谐曲线所预测的反应幅度。我们在这里表明,这种复杂的非线性行为源自兴奋和抑制性抑制的联合时间动态,并提供了全面的数学分析。该研究得出的一个重要预测是,无论刺激变化前的激活状态如何,也无论变化的符号(即刺激是加速还是减速)如何,注意力都会增加瞬态反应的陡峭度。对注意依赖性的速度递增和递减表示的细胞外记录证实了这一预测,并表明改善的变化检测源自经典皮质电路中的基本计算。