Vincent-Lamarre Philippe, Calderini Matias, Thivierge Jean-Philippe
School of Psychology and Center for Neural Dynamics, University of Ottawa, Ottawa, ON, Canada.
Front Comput Neurosci. 2020 Sep 7;14:78. doi: 10.3389/fncom.2020.00078. eCollection 2020.
Many cognitive and behavioral tasks-such as interval timing, spatial navigation, motor control, and speech-require the execution of precisely-timed sequences of neural activation that cannot be fully explained by a succession of external stimuli. We show how repeatable and reliable patterns of spatiotemporal activity can be generated in chaotic and noisy spiking recurrent neural networks. We propose a general solution for networks to autonomously produce rich patterns of activity by providing a multi-periodic oscillatory signal as input. We show that the model accurately learns a variety of tasks, including speech generation, motor control, and spatial navigation. Further, the model performs temporal rescaling of natural spoken words and exhibits sequential neural activity commonly found in experimental data involving temporal processing. In the context of spatial navigation, the model learns and replays compressed sequences of place cells and captures features of neural activity such as the emergence of ripples and theta phase precession. Together, our findings suggest that combining oscillatory neuronal inputs with different frequencies provides a key mechanism to generate precisely timed sequences of activity in recurrent circuits of the brain.
许多认知和行为任务，如间隔计时、空间导航、运动控制和言语，都需要执行精确计时的神经激活序列，而这无法完全由一系列外部刺激来解释。我们展示了如何在混沌和有噪声的脉冲递归神经网络中生成可重复且可靠的时空活动模式。我们提出了一种通用解决方案，通过提供多周期振荡信号作为输入，使网络能够自主产生丰富的活动模式。我们表明，该模型能够准确学习各种任务，包括语音生成、运动控制和空间导航。此外，该模型对自然口语单词进行时间重缩放，并展现出在涉及时间处理的实验数据中常见的序列神经活动。在空间导航的背景下，该模型学习并回放位置细胞的压缩序列，并捕捉神经活动的特征，如涟漪的出现和θ相位进动。总之，我们的研究结果表明，将不同频率的振荡神经元输入相结合，为在大脑递归回路中生成精确计时的活动序列提供了关键机制。
