噪声环境下递归Hebbian网络的鲁棒联想记忆
Robust Associative Memories Naturally Occuring From Recurrent Hebbian Networks Under Noise
摘要：
大脑是一个受能量约束的嘈杂系统。在对人工神经网络建模时，很少考虑这些事实。在本文中，我们有兴趣证明，这些因素实际上可以导致强大的联想记忆的外观。我们首先提出了一个简化的模型，噪声在大脑中，考虑到突触噪声和干扰的神经元外部的网络。当粗量化，我们表明，这种噪声可以减少到插入和擦除。我们采用一个具有可修改的循环连接的神经网络，并将其置于有噪声的外部输入。我们在网络中引入了能量使用限制原则以及合并的Hebbian学习，从而实现了对输入的增量处理。我们表明，自然形成的连接对应于最先进的二进制稀疏联想记忆。
英文摘要：
The brain is a noisy system subject to energy constraints. These facts are rarely taken into account when modelling artificial neural networks. In this paper, we are interested in demonstrating that those factors can actually lead to the appearance of robust associative memories. We first propose a simplified model of noise in the brain, taking into account synaptic noise and interference from neurons external to the network. When coarsely quantized, we show that this noise can be reduced to insertions and erasures. We take a neural network with recurrent modifiable connections, and subject it to noisy external inputs. We introduce an energy usage limitation principle in the network as well as consolidated Hebbian learning, resulting in an incremental processing of inputs. We show that the connections naturally formed correspond to state-of-the-art binary sparse associative memories.
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