Department of Neuroinformatics and Department of Neurophysiology, Donders Institute for Brain, Cognition, and Behaviour, 6525 AJ Nijmegen, Gelderland, The Netherlands
Department of Neuroinformatics, Donders Institute for Brain, Cognition, and Behaviour, 6525 AJ Nijmegen, Gelderland, The Netherlands
Neural Comput. 2019 Sep;31(9):1789-1824. doi: 10.1162/neco_a_01215. Epub 2019 Jul 23.
Behavior is controlled by complex neural networks in which neurons process thousands of inputs. However, even short spike trains evoked in a single cortical neuron were demonstrated to be sufficient to influence behavior in vivo. Specifically, irregular sequences of interspike intervals (ISIs) had a more reliable influence on behavior despite their resemblance to stochastic activity. Similarly, irregular tactile stimulation led to higher rates of behavioral responses. In this study, we identify the mechanisms enabling this sensitivity to stimulus irregularity (SSI) on the neuronal and network levels using simulated spiking neural networks. Matching in vivo experiments, we find that irregular stimulation elicits more detectable network events (bursts) than regular stimulation. Dissecting the stimuli, we identify short ISIs-occurring more frequently in irregular stimulations-as the main drivers of SSI rather than complex irregularity per se. In addition, we find that short-term plasticity modulates SSI. We subsequently eliminate the different mechanisms in turn to assess their role in generating SSI. Removing inhibitory interneurons, we find that SSI is retained, suggesting that SSI is not dependent on inhibition. Removing recurrency, we find that SSI is retained due to the ability of individual neurons to integrate activity over short timescales ("cell memory"). Removing single-neuron dynamics, we find that SSI is retained based on the short-term retention of activity within the recurrent network structure ("network memory"). Finally, using a further simplified probabilistic model, we find that local network structure is not required for SSI. Hence, SSI is identified as a general property that we hypothesize to be ubiquitous in neural networks with different structures and biophysical properties. Irregular sequences contain shorter ISIs, which are the main drivers underlying SSI. The experimentally observed SSI should thus generalize to other systems, suggesting a functional role for irregular activity in cortex.
行为受复杂的神经网络控制，其中神经元处理数千个输入。然而，即使在单个皮质神经元中诱发的短尖峰序列也被证明足以在体内影响行为。具体来说，尽管它们类似于随机活动，但尖峰间间隔（ISI）的不规则序列对行为的影响更可靠。同样，不规则的触觉刺激导致更高的行为反应率。在这项研究中，我们使用模拟的尖峰神经网络在神经元和网络水平上确定了使这种对刺激不规则性（SSI）敏感的机制。与体内实验匹配，我们发现不规则刺激比规则刺激引发更多可检测的网络事件（爆发）。对刺激进行剖析，我们发现短 ISI（在不规则刺激中更频繁发生）是 SSI 的主要驱动因素，而不是复杂的不规则性本身。此外，我们发现短期可塑性调节 SSI。随后，我们逐个消除不同的机制，以评估它们在产生 SSI 中的作用。去除抑制性中间神经元，我们发现 SSI 得以保留，这表明 SSI 不依赖于抑制。去除递归，我们发现由于单个神经元在短时间尺度上整合活动的能力（“细胞记忆”），SSI 得以保留。去除单个神经元动力学，我们发现由于递归网络结构中活动的短期保留（“网络记忆”），SSI 得以保留。最后，使用进一步简化的概率模型，我们发现 SSI 不需要局部网络结构。因此，SSI 被确定为一种普遍存在的特性，我们假设它在具有不同结构和生物物理特性的神经网络中普遍存在。不规则序列包含较短的 ISI，这是 SSI 的主要驱动因素。因此，实验观察到的 SSI 应该推广到其他系统，这表明不规则活动在皮质中具有功能作用。
