Single cortical neurons as deep artificial neural networks
Single cortical neurons as deep artificial neural networks
复制标题
单个皮质神经元作为深度人工神经网络
作者：
D. Beniaguev;Idan Segev;M. London
我们介绍了一种新的方法来研究神经元复杂的I/O信息处理单元，利用机器学习领域的最新进展。我们训练了深度神经网络（DNN）来模拟第5层皮质锥体细胞的详细非线性模型的I/O行为，接收输入突触激活的丰富时空模式。需要一个具有七层的时间卷积DNN（TCN）来准确且非常有效地以毫秒级分辨率捕获该神经元的I/O。这种复杂性主要来自于局部基于NMDA的非线性树枝状电导。DNN的权重矩阵为皮层锥体神经元的I/O功能提供了新的见解，并且所提出的方法可以提供不同神经元类型的功能复杂性的系统表征。我们的研究结果表明，皮层神经元可以被概念化为多层的“深”处理单元，这意味着它们形成的皮层网络具有非经典的架构，并且可能比以前假设的计算能力更强。
We introduce a novel approach to study neurons as sophisticated I/O information processing units by utilizing recent advances in the field of machine learning. We trained deep neural networks (DNNs) to mimic the I/O behavior of a detailed nonlinear model of a layer 5 cortical pyramidal cell, receiving rich spatio-temporal patterns of input synapse activations. A Temporally Convolutional DNN (TCN) with seven layers was required to accurately, and very efficiently, capture the I/O of this neuron at the millisecond resolution. This complexity primarily arises from local NMDA-based nonlinear dendritic conductances. The weight matrices of the DNN provide new insights into the I/O function of cortical pyramidal neurons, and the approach presented can provide a systematic characterization of the functional complexity of different neuron types. Our results demonstrate that cortical neurons can be conceptualized as multi-layered “deep” processing units, implying that the cortical networks they form have a non-classical architecture and are potentially more computationally powerful than previously assumed.
影响因子：
4.3
作者：
Cadena, Santiago A.;Denfield, George H.;Ecker, Alexander S.
通讯作者：
Ecker, Alexander S.