论文标题
兴奋性和抑制性神经元最佳平衡尖峰网络的最小动态
Minimax Dynamics of Optimally Balanced Spiking Networks of Excitatory and Inhibitory Neurons
论文作者
论文摘要
在皮质中观察到激发抑制（E-I）平衡。最近的研究表明，在快速时刻表上的平衡，紧密的平衡和有效的信息编码之间存在着有趣的联系。我们通过采用原则性方法来进一步兴奋（e）和抑制性（i）神经元的最佳平衡网络。通过从基于贪婪的尖峰的最小值目标的优化中得出E-I尖峰神经网络，我们表明，紧密的平衡是由于纠正了与Minimax Optima的偏差。我们通过解决最小值问题，超越平衡网络的统计理论来预测网络中特定的神经元点火率。最后，我们设计了最小值目标，以重建输入信号，关联内存和歧管吸引子的存储，并源自执行计算的E-I网络。总体而言，我们提出了一种用于尖峰E-I网络的新型规范建模方法，这超出了违反Dale定律的广泛使用的能量最小化网络。我们的网络可用于对皮层电路和计算进行建模。
Excitation-inhibition (E-I) balance is ubiquitously observed in the cortex. Recent studies suggest an intriguing link between balance on fast timescales, tight balance, and efficient information coding with spikes. We further this connection by taking a principled approach to optimal balanced networks of excitatory (E) and inhibitory (I) neurons. By deriving E-I spiking neural networks from greedy spike-based optimizations of constrained minimax objectives, we show that tight balance arises from correcting for deviations from the minimax optima. We predict specific neuron firing rates in the network by solving the minimax problem, going beyond statistical theories of balanced networks. Finally, we design minimax objectives for reconstruction of an input signal, associative memory, and storage of manifold attractors, and derive from them E-I networks that perform the computation. Overall, we present a novel normative modeling approach for spiking E-I networks, going beyond the widely-used energy minimizing networks that violate Dale's law. Our networks can be used to model cortical circuits and computations.
