神经元网络自适应Potts模型的最优锁存区域
Optimal Region of Latching Activity in an Adaptive Potts Model for Networks of Neurons
摘要：
在统计力学中，波茨模型（英语：Potts model）是一个描述自旋与两个以上离散态相互作用的模型。具有学习和联想记忆功能的神经网络也可以用Potts自旋系统来建模。从一个离散吸引子状态跳到另一个离散吸引子状态的自发行为（称为闭锁）已经被提出与更高的认知功能相关联。在这里，我们提出了一个模型，其中既有随机动力学的波茨模型和自适应势函数。在噪声（温度）适应参数空间的有限区域中观察到锁存动态。因此，我们建议噪音作为一个基本因素，在这种交替旁边的适应。从动力系统的角度来看，噪声适应交替可能是吸引子模型多稳定性的潜在机制。最后推导出一个适用于实际模型的最优性准则。
英文摘要：
In statistical mechanics, the Potts model is a model for interacting spins with more than two discrete states. Neural networks which exhibit features of learning and associative memory can also be modeled by a system of Potts spins. A spontaneous behavior of hopping from one discrete attractor state to another (referred to as latching) has been proposed to be associated with higher cognitive functions. Here we propose a model in which both the stochastic dynamics of Potts models and an adaptive potential function are present. A latching dynamics is observed in a limited region of the noise(temperature)-adaptation parameter space. We hence suggest noise as a fundamental factor in such alternations alongside adaptation. From a dynamical systems point of view, the noise-adaptation alternations may be the underlying mechanism for multi-stability in attractor based models. An optimality criterion for realistic models is finally inferred.
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