Training dynamically balanced excitatory-inhibitory networks
Training dynamically balanced excitatory-inhibitory networks
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DOI：
10.1371/journal.pone.0220547
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发表时间：
2019-08-08
期刊：
影响因子：
3.7
通讯作者：
Abbott, L. F.
中科院分区：
文献类型：
--
作者：
Ingrosso, Alessandro;Abbott, L. F.
神经回路的生物学合理模型的构建对于理解神经系统的计算特性至关重要。构建由遵守戴尔定律的独立兴奋性和抑制性神经元组成的功能网络提出了许多挑战。我们展示了如何基于目标的方法，结合快速在线约束优化技术，是能够建立功能模型的速率和尖峰循环神经网络中的激励和抑制是平衡的。平衡网络可以被训练成产生复杂的时间模式，并解决输入输出任务，同时保留生物学上理想的特征，如戴尔定律和反应变异性。
The construction of biologically plausible models of neural circuits is crucial for understanding the computational properties of the nervous system. Constructing functional networks composed of separate excitatory and inhibitory neurons obeying Dale's law presents a number of challenges. We show how a target-based approach, when combined with a fast online constrained optimization technique, is capable of building functional models of rate and spiking recurrent neural networks in which excitation and inhibition are balanced. Balanced networks can be trained to produce complicated temporal patterns and to solve input-output tasks while retaining biologically desirable features such as Dale's law and response variability.