基本哺乳动物视网膜对原型复杂细胞CNN通用机的影响

D. Bálya, C. Rekeczky, T. Roska
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引用次数: 8

摘要

描述了在可编程硅芯片上重建视觉系统第一阶段的独特可能性。所建立的哺乳动物视网膜模型可以作为一种模拟算法在原型复杂细胞神经网络处理器上实现。它使神经生物学和视觉研究界能够实时研究生物视觉转换设计的智慧。操作原型复杂细胞CNN-UM处理器为工程和计算神经科学界打开了一个新的世界。本文演示了朝这个方向迈出的第一步。在这里,我们提出了一个视网膜通道的分解和缩放作为一个硬件级CNN-UM算法。该模拟算法由一系列不同的复杂单元CNN时空动态组成,在最近制作的复杂单元CNN- um原型芯片上是可行的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Basic mammalian retinal effects on the prototype complex cell CNN universal machine
The unique possibility for reconstructing the first stage of the visual system on a programmable silicon chip is described. The developed mammalian retinal model can be implemented as an analogic algorithm running on a prototype complex cell cellular neural network processor. It enables the neuro-biological and vision research communities to study the wisdom of biological visual transformations design in real-time. The operating prototype complex-cell CNN-UM processor opens a new world for the engineering as well as the computational neuroscience communities. This paper demonstrates the first steps in this direction. Here we present the decomposition and scaling of one retinal channel as a hardware-level CNN-UM algorithm. The analogic algorithm consists of a series of different complex-cell CNN spatial-temporal dynamics, feasible on the recently fabricated prototype complex cell CNN-UM chip.
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