Mi Yuanyuan, Lin Xiaohan, Wu Si
Brain Science Center, Institute of Basic Medical SciencesBeijing, China; State Key Lab of Cognitive Neuroscience and Learning, IDG/McGovern Institute for Brain Research, Beijing Normal UniversityBeijing, China.
State Key Lab of Cognitive Neuroscience and Learning, IDG/McGovern Institute for Brain Research, Beijing Normal University Beijing, China.
Front Comput Neurosci. 2016 Sep 13;10:96. doi: 10.3389/fncom.2016.00096. eCollection 2016.
Neural systems display rich short-term dynamics at various levels, e.g., spike-frequency adaptation (SFA) at the single-neuron level, and short-term facilitation (STF) and depression (STD) at the synapse level. These dynamical features typically cover a broad range of time scales and exhibit large diversity in different brain regions. It remains unclear what is the computational benefit for the brain to have such variability in short-term dynamics. In this study, we propose that the brain can exploit such dynamical features to implement multiple seemingly contradictory computations in a single neural circuit. To demonstrate this idea, we use continuous attractor neural network (CANN) as a working model and include STF, SFA and STD with increasing time constants in its dynamics. Three computational tasks are considered, which are persistent activity, adaptation, and anticipative tracking. These tasks require conflicting neural mechanisms, and hence cannot be implemented by a single dynamical feature or any combination with similar time constants. However, with properly coordinated STF, SFA and STD, we show that the network is able to implement the three computational tasks concurrently. We hope this study will shed light on the understanding of how the brain orchestrates its rich dynamics at various levels to realize diverse cognitive functions.
神经系统在各个层面都展现出丰富的短期动态，例如，在单神经元层面的放电频率适应（SFA），以及在突触层面的短期易化（STF）和抑制（STD）。这些动态特征通常涵盖广泛的时间尺度，并且在不同脑区表现出很大的多样性。目前尚不清楚大脑具有这种短期动态变化的计算益处是什么。在本研究中，我们提出大脑可以利用这些动态特征在单个神经回路中实现多种看似相互矛盾的计算。为了证明这一想法，我们使用连续吸引子神经网络（CANN）作为工作模型，并在其动态中纳入具有递增时间常数的STF、SFA和STD。考虑了三个计算任务，即持续活动、适应和预期跟踪。这些任务需要相互冲突的神经机制，因此不能由单一动态特征或任何具有相似时间常数的组合来实现。然而，通过适当协调的STF、SFA和STD，我们表明该网络能够同时实现这三个计算任务。我们希望这项研究将有助于理解大脑如何在各个层面协调其丰富的动态以实现多样的认知功能。
