论文标题
从人工智能到大脑智能：类似脑智能的基础学习和记忆算法
From Artificial Intelligence to Brain Intelligence: The basis learning and memory algorithm for brain-like intelligence
论文作者
论文摘要
脑学习和记忆的算法仍未确定。人造神经网络的反向传播算法被认为不适合脑皮质，并且缺乏用于记忆的算法。我们设计了一个大脑版本的反向传播算法，它们在生物学上是合理的，可以用虚拟神经元实现以完成图像分类任务。提出了一种可以自动分配Engram单元格的编码算法，这是一种用于内存Engram理论的算法实现，并可以模拟海马如何实现快速的关联内存。 LTP和LTD在小脑中的作用也以算法水平解释。我们的结果提出了一种大脑部署反向传播算法的方法，以及用于内存Engram理论的稀疏编码方法。
The algorithm of brain learning and memory is still undetermined. The backpropagation algorithm of artificial neural networks was thought not suitable for brain cortex, and there is a lack of algorithm for memory engram. We designed a brain version of backpropagation algorithm, which are biologically plausible and could be implemented with virtual neurons to complete image classification task. An encoding algorithm that can automatically allocate engram cells is proposed, which is an algorithm implementation for memory engram theory, and could simulate how hippocampus achieve fast associative memory. The role of the LTP and LTD in the cerebellum is also explained in algorithm level. Our results proposed a method for the brain to deploy backpropagation algorithm, and sparse coding method for memory engram theory.
