The brain processes underlying cognitive tasks must be very robust. Disruptions such as the destruction of large numbers of neurons, or the impact of alcohol and lack of sleep do not have negative effects except when they occur in an extreme form. This robustness implies that the parameters determining the functioning of networks of individual neurons must have large ranges or there must exist stabilizing mechanisms that keep the functioning of a network within narrow bounds. The simulation of a minimal neuronal architecture necessary to study cognitive tasks is described, which consists of a loop of three cell-assemblies. A crucial factor in this architecture is the critical threshold of a cell-assembly. When activated at a level above the critical threshold, the activation in a cell-assembly is subject to autonomous growth, which leads to an oscillation in the loop. When activated below the critical threshold, excitation gradually extinguishes. In order to circumvent the large parameter space of spiking neurons, a rate-dependent model of neuronal firing was chosen. The resulting parameter space of 12 parameters was explored by means of a genetic algorithm. The ranges of the parameters for which the architecture produced the required oscillations and extinctions, turned out to be relatively narrow. These ranges remained narrow when a stabilizing mechanism, controlling the total amount of activation, was introduced. The architecture thus shows chaotic behaviour. Given the overall stability of the operation of the brain, it can be concluded that there must exist other mechanisms that make the network robust. Three candidate mechanisms are discussed: synaptic scaling, synaptic homeostasis, and the synchronization of neural spikes.

中文翻译：

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记忆痕迹的性质及其神经计算意义。

大脑处理潜在的认知任务必须非常强大。破坏，例如大量神经元的破坏，或酒精和睡眠不足的影响，除非以极端形式发生，否则不会产生负面影响。这种稳健性意味着决定单个神经元网络功能的参数必须有很大的范围，或者必须存在将网络功能保持在狭窄范围内的稳定机制。描述了研究认知任务所需的最小神经元结构的模拟，它由三个细胞组件的循环组成。这种架构中的一个关键因素是细胞组装的临界阈值。当激活水平高于临界阈值时，细胞组装中的激活会自动生长，这导致环路中的振荡。当在临界阈值以下激活时，兴奋逐渐消失。为了规避尖峰神经元的大参数空间，选择了神经元放电的速率依赖模型。通过遗传算法探索得到的 12 个参数的参数空间。架构产生所需振荡和消光的参数范围相对较窄。当引入控制激活总量的稳定机制时，这些范围仍然很窄。因此，该架构显示出混乱的行为。考虑到大脑运行的整体稳定性，可以得出结论，一定存在其他机制使网络健壮。讨论了三种候选机制：突触缩放、