无需微调即可寻找长时间尺度
Searching for long time scales without fine tuning
AI总结：
本研究结合最大熵和随机矩阵理论方法构建神经网络系综，探索产生长时间尺度所需的约束条件，发现单一长时间尺度可普遍产生但完整慢模式谱需更多微调。
摘要：
大多数动物和人类行为发生的时间尺度远长于单个神经元的响应时间。在许多情况下，这些长时间尺度很可能源于神经元网络中电活动的循环动力学。在线性模型中，时间尺度由动力学矩阵的特征值设定，该矩阵的元素衡量神经元之间突触连接的强度。目前尚不清楚这些矩阵元素需要在多大程度上进行微调才能产生长时间尺度；在某些情况下，需要的不仅是单一长时间尺度，而是整个范围。从随机对称连接的最简单情况出发，我们结合最大熵和随机矩阵理论方法构建网络系综，探索长时间尺度成为普遍现象所需的约束条件。我们认为，单一长时间尺度可以从现实约束中普遍产生，但完整的慢模式谱需要更多微调。生成从这些系综中提取的突触连接模式的Langevin动力学涉及Hebbian学习和活动依赖性突触缩放的结合。
英文摘要：
Most of animal and human behavior occurs on time scales much longer than the response times of individual neurons. In many cases, it is plausible that these long time scales emerge from the recurrent dynamics of electrical activity in networks of neurons. In linear models, time scales are set by the eigenvalues of a dynamical matrix whose elements measure the strengths of synaptic connections between neurons. It is not clear to what extent these matrix elements need to be tuned in order to generate long time scales; in some cases, one needs not just a single long time scale but a whole range. Starting from the simplest case of random symmetric connections, we combine maximum entropy and random matrix theory methods to construct ensembles of networks, exploring the constraints required for long time scales to become generic. We argue that a single long time scale can emerge generically from realistic constraints, but a full spectrum of slow modes requires more tuning. Langevin dynamics that generates patterns of synaptic connections drawn from these ensembles involves a combination of Hebbian learning and activity-dependent synaptic scaling.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
