The critical state is assumed to be optimal for any computation in recurrent neural networks, because criticality maximizes a number of abstract computational properties. We challenge this assumption by evaluating the performance of a spiking recurrent neural network on a set of tasks of varying complexity at - and away from critical network dynamics. To that end, we developed a spiking network with synaptic plasticity on a neuromorphic chip. We show that the distance to criticality can be easily adapted by changing the input strength, and then demonstrate a clear relation between criticality, task-performance and information-theoretic fingerprint. Whereas the information-theoretic measures all show that network capacity is maximal at criticality, this is not the case for performance on specific tasks: Only the complex, memory-intensive tasks profit from criticality, whereas the simple tasks suffer from it. Thereby, we challenge the general assumption that criticality would be beneficial for any task, and provide instead an understanding of how the collective network state should be tuned to task requirement to achieve optimal performance.

中文翻译：

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具有可塑性的尖峰神经形态网络的临界性和计算控制

临界状态被假定为对于循环神经网络中的任何计算都是最佳的，因为临界状态最大化了许多抽象的计算特性。我们通过评估尖峰循环神经网络在一组具有不同复杂性的任务上的性能来挑战这一假设 - 和远离关键网络动态。为此，我们在神经形态芯片上开发了一个具有突触可塑性的尖峰网络。我们表明，通过改变输入强度可以很容易地调整与临界的距离，然后证明临界、任务性能和信息理论指纹之间的明确关系。尽管信息论测量都表明网络容量在临界状态下最大，但在特定任务上的性能并非如此：只有复杂的，内存密集型任务受益于关键性，而简单的任务则受到影响。因此，我们挑战了关键性对任何任务都有益的一般假设，而是提供了对如何将集体网络状态调整到任务要求以实现最佳性能的理解。