Computational Neuroscience, Department of Child and Adolescent Psychiatry, Faculty of Medicine, Technische Universität Dresden, Dresden 01307, Germany.
Department of Physiology of Cognitive Processes, Max Planck Institute for Biological Cybernetics, Tübingen 72076, Germany.
Proc Natl Acad Sci U S A. 2024 Oct 8;121(41):e2302730121. doi: 10.1073/pnas.2302730121. Epub 2024 Oct 1.
The critical brain hypothesis states that the brain can benefit from operating close to a second-order phase transition. While it has been shown that several computational aspects of sensory processing (e.g., sensitivity to input) can be optimal in this regime, it is still unclear whether these computational benefits of criticality can be leveraged by neural systems performing behaviorally relevant computations. To address this question, we investigate signatures of criticality in networks optimized to perform efficient coding. We consider a spike-coding network of leaky integrate-and-fire neurons with synaptic transmission delays. Previously, it was shown that the performance of such networks varies nonmonotonically with the noise amplitude. Interestingly, we find that in the vicinity of the optimal noise level for efficient coding, the network dynamics exhibit some signatures of criticality, namely, scale-free dynamics of the spiking and the presence of crackling noise relation. Our work suggests that two influential, and previously disparate theories of neural processing optimization (efficient coding and criticality) may be intimately related.
关键大脑假说指出，大脑可以从接近二阶相变的状态中受益。虽然已经表明，感觉处理的几个计算方面（例如，对输入的敏感性）在这种状态下可以达到最优，但仍不清楚这种临界状态的计算优势是否可以被执行行为相关计算的神经系统利用。为了解决这个问题，我们研究了优化后用于有效编码的网络中的临界特征。我们考虑了一个具有突触传递延迟的漏积分和放电神经元的尖峰编码网络。此前已经表明，这种网络的性能随噪声幅度呈非单调变化。有趣的是，我们发现，在有效编码的最佳噪声水平附近，网络动力学表现出一些临界特征，即尖峰的无标度动力学和噼啪噪声关系的存在。我们的工作表明，两种有影响力的、以前不同的神经处理优化理论（有效编码和临界状态）可能密切相关。
