Philips Ryan T, Chhabria Karishma, Chakravarthy V Srinivasa
Computational Neuroscience Laboratory, Department of Biotechnology, Indian Institute of Technology Madras Chennai, India.
Front Neural Circuits. 2016 Feb 26;10:7. doi: 10.3389/fncir.2016.00007. eCollection 2016.
Cerebral vascular dynamics are generally thought to be controlled by neural activity in a unidirectional fashion. However, both computational modeling and experimental evidence point to the feedback effects of vascular dynamics on neural activity. Vascular feedback in the form of glucose and oxygen controls neuronal ATP, either directly or via the agency of astrocytes, which in turn modulates neural firing. Recently, a detailed model of the neuron-astrocyte-vessel system has shown how vasomotion can modulate neural firing. Similarly, arguing from known cerebrovascular physiology, an approach known as "hemoneural hypothesis" postulates functional modulation of neural activity by vascular feedback. To instantiate this perspective, we present a computational model in which a network of "vascular units" supplies energy to a neural network. The complex dynamics of the vascular network, modeled by a network of oscillators, turns neurons ON and OFF randomly. The informational consequence of such dynamics is explored in the context of an auto-encoder network. In the proposed model, each vascular unit supplies energy to a subset of hidden neurons of an autoencoder network, which constitutes its "projective field." Neurons that receive adequate energy in a given trial have reduced threshold, and thus are prone to fire. Dynamics of the vascular network are governed by changes in the reconstruction error of the auto-encoder network, interpreted as the neuronal demand. Vascular feedback causes random inactivation of a subset of hidden neurons in every trial. We observe that, under conditions of desynchronized vascular dynamics, the output reconstruction error is low and the feature vectors learnt are sparse and independent. Our earlier modeling study highlighted the link between desynchronized vascular dynamics and efficient energy delivery in skeletal muscle. We now show that desynchronized vascular dynamics leads to efficient training in an auto-encoder neural network.
脑血管动力学通常被认为是以单向方式由神经活动控制。然而，计算建模和实验证据均指向血管动力学对神经活动的反馈作用。葡萄糖和氧气形式的血管反馈直接或通过星形胶质细胞间接控制神经元的三磷酸腺苷（ATP），进而调节神经放电。最近，一个详细的神经元 - 星形胶质细胞 - 血管系统模型展示了血管运动如何调节神经放电。同样，基于已知的脑血管生理学，一种被称为“血神经假说”的方法假定血管反馈对神经活动进行功能调节。为了实例化这一观点，我们提出了一个计算模型，其中“血管单元”网络为神经网络提供能量。由振荡器网络建模的血管网络的复杂动力学随机地开启和关闭神经元。在自编码器网络的背景下探索这种动力学的信息后果。在所提出的模型中，每个血管单元为自编码器网络的一部分隐藏神经元提供能量，这些隐藏神经元构成其“投射场”。在给定试验中获得足够能量的神经元阈值降低，因此易于放电。血管网络的动力学受自编码器网络重建误差变化的支配，该误差被解释为神经元需求。血管反馈在每次试验中导致一部分隐藏神经元随机失活。我们观察到，在血管动力学去同步的条件下，输出重建误差较低，并且学习到的特征向量稀疏且独立。我们早期的建模研究强调了去同步血管动力学与骨骼肌中高效能量传递之间的联系。我们现在表明，去同步血管动力学导致自编码器神经网络中的高效训练。
