Generative neural networks can produce data samples according to the statistical properties of their training distribution. This feature can be used to test modern computational neuroscience hypotheses suggesting that spontaneous brain activity is partially supported by top-down generative processing. A widely studied class of generative models is that of Restricted Boltzmann Machines (RBMs), which can be used as building blocks for unsupervised deep learning architectures. In this work, we systematically explore the generative dynamics of RBMs, characterizing the number of states visited during top-down sampling and investigating whether the heterogeneity of visited attractors could be increased by starting the generation process from biased hidden states. By considering an RBM trained on a classic dataset of handwritten digits, we show that the capacity to produce diverse data prototypes can be increased by initiating top-down sampling from chimera states, which encode high-level visual features of multiple digits. We also found that the model is not capable of transitioning between all possible digit states within a single generation trajectory, suggesting that the top-down dynamics is heavily constrained by the shape of the energy function.

翻译：生成式神经网络能够根据其训练数据的统计特性生成数据样本。这一特性可用于检验现代计算神经科学的假设，即大脑的自发活动部分由自上而下的生成加工支持。受限玻尔兹曼机（RBM）是一类被广泛研究的生成模型，可作为无监督深度学习架构的基础组件。在本研究中，我们系统探索了RBM的生成动力学，刻画了自上而下采样过程中访问的状态数量，并考察了通过从偏置隐状态启动生成过程是否能增加所访问吸引子的异质性。通过考虑一个在经典手写数字数据集上训练的RBM，我们证明：从编码多个数字高层视觉特征的嵌合态启动自上而下采样，可提升生成多样数据原型的能力。我们还发现，该模型无法在单条生成轨迹中遍历所有可能的数字状态，这表明自上而下动力学受到能量函数形状的强烈约束。