In the complex domain of neural information processing, discerning fundamental principles from ancillary details remains a significant challenge. While there is extensive knowledge about the anatomy and physiology of the early visual system, a comprehensive computational theory remains elusive. Can we gain insights into the underlying principles of a biological system by abstracting away from its detailed implementation and focusing on the fundamental problems that the system is designed to solve? Utilizing an abstract model based on minimal yet realistic assumptions, we show how to achieve the early visual system's two ultimate objectives: efficient information transmission and sensor probability distribution modeling. We show that optimizing for information transmission does not yield optimal probability distribution modeling. We illustrate, using a two-pixel (2D) system and image patches, that an efficient representation can be realized via nonlinear population code driven by two types of biologically plausible loss functions that depend solely on output. After unsupervised learning, our abstract IPU model bears remarkable resemblances to biological systems, despite not mimicking many features of real neurons, such as spiking activity. A preliminary comparison with a contemporary deep learning model suggests that the IPU model offers a significant efficiency advantage. Our model provides novel insights into the computational theory of early visual systems as well as a potential new approach to enhance the efficiency of deep learning models.

翻译：在神经信息处理这一复杂领域中，从辅助细节中辨别基本原理仍是一项重大挑战。尽管我们对早期视觉系统的解剖结构和生理功能已有广泛认知，但完整的计算理论仍不明确。能否通过抽象化系统的具体实现细节、聚焦于系统旨在解决的根本问题，从而洞悉生物系统的基本原理？我们基于最小且现实的假设构建了一个抽象模型，并展示如何实现早期视觉系统的两大终极目标：高效信息传输与传感器概率分布建模。研究表明，优化信息传输并不能实现最优概率分布建模。我们通过一个双像素（二维）系统与图像斑块示例阐明，采用仅依赖于输出的两类生物可解释损失函数驱动的非线性群体编码，即可实现高效表征。经过无监督学习，尽管我们的抽象IPU模型未模拟真实神经元的诸多特征（如脉冲活动），却与生物系统展现出显著相似性。与当代深度学习模型的初步比较表明，该IPU模型在效率上具有显著优势。该模型不仅为早期视觉系统的计算理论提供了新见解，也为提升深度学习模型效率提供了潜在新途径。