Liquid state machine (LSM) is a type of recurrent spiking network with a strong relationship to neurophysiology and has achieved great success in time series processing. However, the computational cost of simulations and complex dynamics with time dependency limit the size and functionality of LSMs. This paper presents a large-scale bioinspired LSM with modular topology. We integrate the findings on the visual cortex that specifically designed input synapses can fit the activation of the real cortex and perform the Hough transform, a feature extraction algorithm used in digital image processing, without additional cost. We experimentally verify that such a combination can significantly improve the network functionality. The network performance is evaluated using the MNIST dataset where the image data are encoded into spiking series by Poisson coding. We show that the proposed structure can not only significantly reduce the computational complexity but also achieve higher performance compared to the structure of previous reported networks of a similar size. We also show that the proposed structure has better robustness against system damage than the small-world and random structures. We believe that the proposed computationally efficient method can greatly contribute to future applications of reservoir computing.

液体状态机（LSM）是一种递归尖峰网络，与神经生理学有很强的关系，并且在时间序列处理中取得了巨大的成功。但是，仿真的计算成本和具有时间依赖性的复杂动力学限制了LSM的大小和功能。本文提出了具有模块化拓扑结构的大规模生物启发式LSM。我们将视觉皮层上的发现整合在一起，即专门设计的输入突触可适合真实皮层的激活并执行霍夫变换（一种用于数字图像处理的特征提取算法），而无需支付额外费用。我们通过实验验证了这种组合可以显着改善网络功能。使用MNIST数据集评估网络性能，其中通过Poisson编码将图像数据编码为尖峰序列。我们表明，与先前报道的类似规模的网络结构相比，提出的结构不仅可以显着降低计算复杂度，而且可以获得更高的性能。我们还表明，与小世界和随机结构相比，所提出的结构对系统损坏具有更好的鲁棒性。我们认为，所提出的高效计算方法可以极大地促进储层计算的未来应用。我们还表明，与小世界和随机结构相比，所提出的结构对系统损坏具有更好的鲁棒性。我们认为，所提出的高效计算方法可以极大地促进储层计算的未来应用。我们还表明，与小世界和随机结构相比，所提出的结构对系统损坏具有更好的鲁棒性。我们认为，所提出的高效计算方法可以极大地促进储层计算的未来应用。