Department for Electrical Engineering and Computer Science, Technische Universität Berlin, Berlin, Germany.
Bernstein Center for Computational Neuroscience, Berlin, Germany.
PLoS Comput Biol. 2021 Nov 8;17(11):e1009478. doi: 10.1371/journal.pcbi.1009478. eCollection 2021 Nov.
Cortical pyramidal cells (PCs) have a specialized dendritic mechanism for the generation of bursts, suggesting that these events play a special role in cortical information processing. In vivo, bursts occur at a low, but consistent rate. Theory suggests that this network state increases the amount of information they convey. However, because burst activity relies on a threshold mechanism, it is rather sensitive to dendritic input levels. In spiking network models, network states in which bursts occur rarely are therefore typically not robust, but require fine-tuning. Here, we show that this issue can be solved by a homeostatic inhibitory plasticity rule in dendrite-targeting interneurons that is consistent with experimental data. The suggested learning rule can be combined with other forms of inhibitory plasticity to self-organize a network state in which both spikes and bursts occur asynchronously and irregularly at low rate. Finally, we show that this network state creates the network conditions for a recently suggested multiplexed code and thereby indeed increases the amount of information encoded in bursts.
皮质锥体细胞 (PCs) 具有专门的树突爆发产生机制，表明这些事件在皮质信息处理中起着特殊的作用。在体内，爆发以低但一致的速率发生。理论表明，这种网络状态增加了它们传递的信息量。然而，由于爆发活动依赖于阈值机制，因此它对树突输入水平非常敏感。在尖峰网络模型中，爆发很少发生的网络状态通常不太稳健，而是需要精细调整。在这里，我们表明，通过针对树突的抑制性中间神经元的一种内稳态抑制性可塑性规则可以解决这个问题，该规则与实验数据一致。所提出的学习规则可以与其他形式的抑制性可塑性相结合，以自组织一种网络状态，其中尖峰和爆发以异步和不规则的方式以低速率发生。最后，我们表明，这种网络状态为最近提出的复用代码创造了网络条件，从而确实增加了爆发中编码的信息量。
