Department of Mathematics, University of Houston, Houston, Texas, United States of America.
Department of Applied and Computational Mathematics and Statistics, University of Notre Dame, Notre Dame, Indiana, United States of America.
PLoS Comput Biol. 2021 May 12;17(5):e1008958. doi: 10.1371/journal.pcbi.1008958. eCollection 2021 May.
The dynamics of local cortical networks are irregular, but correlated. Dynamic excitatory-inhibitory balance is a plausible mechanism that generates such irregular activity, but it remains unclear how balance is achieved and maintained in plastic neural networks. In particular, it is not fully understood how plasticity induced changes in the network affect balance, and in turn, how correlated, balanced activity impacts learning. How do the dynamics of balanced networks change under different plasticity rules? How does correlated spiking activity in recurrent networks change the evolution of weights, their eventual magnitude, and structure across the network? To address these questions, we develop a theory of spike-timing dependent plasticity in balanced networks. We show that balance can be attained and maintained under plasticity-induced weight changes. We find that correlations in the input mildly affect the evolution of synaptic weights. Under certain plasticity rules, we find an emergence of correlations between firing rates and synaptic weights. Under these rules, synaptic weights converge to a stable manifold in weight space with their final configuration dependent on the initial state of the network. Lastly, we show that our framework can also describe the dynamics of plastic balanced networks when subsets of neurons receive targeted optogenetic input.
局部皮质网络的动力学是不规则的，但具有相关性。动态的兴奋-抑制平衡是产生这种不规则活动的一种合理机制，但在可塑性神经网络中，平衡是如何实现和维持的仍然不清楚。特别是，网络中可塑性诱导的变化如何影响平衡，以及相关的、平衡的活动如何影响学习，这些问题还没有完全弄清楚。在不同的可塑性规则下，平衡网络的动力学如何变化？在递归网络中，相关的尖峰活动如何改变权重的演变、它们在网络中的最终幅度和结构？为了解决这些问题，我们在平衡网络中提出了一个关于尖峰时间依赖性可塑性的理论。我们表明，在可塑性诱导的权重变化下，可以实现和维持平衡。我们发现，输入的相关性轻微影响突触权重的演变。在某些可塑性规则下，我们发现在放电率和突触权重之间出现相关性。在这些规则下，突触权重在权重空间中收敛到一个稳定流形，其最终配置取决于网络的初始状态。最后，我们表明，当神经元的子集接收靶向光遗传学输入时，我们的框架也可以描述可塑性平衡网络的动力学。
