标题： 无强外部电流下的自维持平衡状态理论 显示英文标题

标题： A theory for self-sustained balanced states in absence of strong external currents

摘要： 具有平衡兴奋和抑制的循环神经网络表现出不规则的异步动力学，这对于皮层计算是基本的。 经典平衡机制需要强烈的外部输入来维持有限的放电率，这引发了对其生物学合理性的担忧。 在这里，我们研究了一种基于短时突触抑制（STD）的替代机制，该机制作用于兴奋性-兴奋性突触，能够在不需要外部输入的情况下动态平衡网络活动。 通过使用数值模拟和理论研究，我们表征了一个由$N$速率神经元模型组成的大量耦合网络的动力学。 根据突触强度$J_0$，网络表现出两种不同的状态：在足够小的$J_0$时，它收敛到一个均匀的固定点，而在足够大的$J_0$时，它表现出速率混沌。 对于有限的网络，我们观察到几种不同的通向混沌的路径，这取决于网络的实现方式。 分离均匀稳定解与速率混沌的过渡区域的宽度似乎随着$N$的增加而缩小，并最终在热力学极限中消失。 通过采用动力学平均场方法对速率混沌状态进行表征，一方面使我们确认这种新的平衡机制能够在热力学极限下维持有限的不规则活动，另一方面揭示了平衡是通过动态抵消由大量耦合产生的输入相关性来实现的。 我们的发现表明，STD为平衡网络提供了一种内在的自我调节机制，在不需要生物上不现实的输入的情况下维持不规则但稳定的活动。 这项工作扩展了平衡网络范式，提供了关于皮层电路如何通过突触适应保持鲁棒动力学的见解。 显示英文摘要

摘要： Recurrent neural networks with balanced excitation and inhibition exhibit irregular asynchronous dynamics, which is fundamental for cortical computations. Classical balance mechanisms require strong external inputs to sustain finite firing rates, raising concerns about their biological plausibility. Here, we investigate an alternative mechanism based on short-term synaptic depression (STD) acting on excitatory-excitatory synapses, which dynamically balances the network activity without the need of external inputs. By employing numerical simulations and theoretical investigations we characterize the dynamics of a massively coupled network made up of $N$ rate-neuron models. Depending on the synaptic strength $J_0$, the network exhibits two distinct regimes: at sufficiently small $J_0$, it converges to a homogeneous fixed point, while for sufficiently large $J_0$, it exhibits Rate Chaos. For finite networks, we observe several different routes to chaos depending on the network realization. The width of the transition region separating the homogeneous stable solution from Rate Chaos appears to shrink for increasing $N$ and eventually to vanish in the thermodynamic limit. The characterization of the Rate Chaos regime performed by employing Dynamical Mean Field approaches allow us on one side to confirm that this novel balancing mechanism is able to sustain finite irregular activity even in the thermodynamic limit, and on the other side to reveal that the balancing occurs via dynamic cancellation of the input correlations generated by the massive coupling. Our findings show that STD provides an intrinsic self-regulating mechanism for balanced networks, sustaining irregular yet stable activity without the need of biologically unrealistic inputs. This work extends the balanced network paradigm, offering insights into how cortical circuits could maintain robust dynamics via synaptic adaptation.