Democratic reinforcement: A principle for brain function.
Democratic reinforcement: A principle for brain function.
复制标题
民主强化：大脑功能的原则。
DOI：
--
复制
发表时间：
1995
期刊：
影响因子：
--
通讯作者：
P. Bak
中科院分区：
文献类型：
--
作者：
D. Stassinopoulos;P. Bak
我们介绍一个简单的“玩具”大脑模型。该模型由一组随机连接的、或分层的整合与激发神经元组成。环境的输入和输出随机连接到神经元的子集。放电神经元之间的联系根据动作的成功与否而增强或减弱。与以前的强化学习算法不同，来自环境的反馈是民主的：它以相同的方式影响所有神经元，无论它们在网络中的位置如何，也与输出信号无关。因此，不需要不切实际的反向传播或其他外部计算。这是通过一个全局阈值调节来实现的，它允许系统自组织到一个高度敏感的、可能是具有低活动和放电神经元之间稀疏连接的“关键”状态。低活跃度使静止区域的记忆得以保存，因为在教授新信息时，只有放电神经元会被修改。
We introduce a simple ``toy`` brain model. The model consists of a set of randomly connected, or layered integrate-and-fire neurons. Inputs to and outputs from the environment are connected randomly to subsets of neurons. The connections between firing neurons are strengthened or weakened according to whether the action was successful or not. Unlike previous reinforcement learning algorithms, the feedback from the environment is democratic: it affects all neurons in the same way, irrespective of their position in the network and independent of the output signal. Thus no unrealistic back propagation or other external computation is needed. This is accomplished by a global threshold regulation which allows the system to self-organize into a highly susceptible, possibly ``critical`` state with low activity and sparse connections between firing neurons. The low activity permits memory in quiescent areas to be conserved since only firing neurons are modified when new information is being taught.