预测编码作为反向传播的神经形态替代方案:批判性评估
Umais Zahid1, Qinghai Guo2, Zafeirios Fountas3
1Huawei Technologies R&D, London N19 3HT, U.K. umais.zahid@huawei.com.
Neural computation
|October 16, 2023
概括
预测编码 (PC) 可能不会像最初希望的那样取代深度学习中的反向传播. 当前的PC变体具有计算复杂性的下限,其下限由反向传播,限制了它们在神经形态系统中的使用.
科学领域:
- 计算神经科学是一种神经科学.
- 深度学习 (Deep Learning) 是一种深度学习.
- 机器学习算法 机器学习算法
背景情况:
- 逆向传播是深度学习中信用分配的标准算法.
- 由于类似的参数更新,预测编码 (PC) 变体已经成为潜在的替代方案.
- 已经突出了PC的神经生物学可信性和神经形态系统的潜力.
研究的目的:
- 调查那些声称预测编码 (PC) 可以作为反向传播的可行替代方案的说法.
- 分析当代PC变体的计算复杂性和神经生物学可信性.
- 在深度学习的背景下,澄清PC和反向传播之间的关系.
主要方法:
- 对各种当代预测编码 (PC) 变体的时间复杂性边界的分析.
- 将这些边界与反向传播的复杂性进行比较.
- 检查PC变体关于神经生物学可信性和变异贝叶斯解释的特性.
主要成果:
- 发现PC变体的时间复杂度极限是通过反向传播的下限.
- 确定了PC变体的关键特性,并对它们的神经生物学解释产生了影响.
- 以现有的PC形式直接取代反向传播似乎比以前建议的更为有限.
结论:
- 当前的预测编码 (PC) 变体表现出计算复杂性,但不超过反向传播.
- 这些发现缓和了对PC作为深度学习和神经形态应用中反向传播的直接,有利的替代品的期望.
- 需要进一步的研究才能充分理解PC在人工智能的潜力和局限性.
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