论文标题
依赖爆发的可塑性和树突扩增支持基于目标的学习和分层模仿学习
Burst-dependent plasticity and dendritic amplification support target-based learning and hierarchical imitation learning
论文作者
论文摘要
大脑可以学会以高时间和精力充沛的效率来解决广泛的任务。但是，大多数生物学模型由简单的单个隔室神经元组成，无法实现人工智能的最新性能。我们提出了一个多室神经元的多室模型，其中爆发和树突状输入隔离使可能有可能支持基于生物靶向的学习。在基于目标的学习中，向网络提出了问题的内部解决方案（在我们的情况下是爆发的时空时间模式），绕过了错误回传和信用分配的问题。最后，我们表明，这种神经元建筑自然支持层次模仿学习的编排，从而使挑战长期决策任务的分解能够分解为更简单的子任务。
The brain can learn to solve a wide range of tasks with high temporal and energetic efficiency. However, most biological models are composed of simple single compartment neurons and cannot achieve the state-of-art performances of artificial intelligence. We propose a multi-compartment model of pyramidal neuron, in which bursts and dendritic input segregation give the possibility to plausibly support a biological target-based learning. In target-based learning, the internal solution of a problem (a spatio temporal pattern of bursts in our case) is suggested to the network, bypassing the problems of error backpropagation and credit assignment. Finally, we show that this neuronal architecture naturally support the orchestration of hierarchical imitation learning, enabling the decomposition of challenging long-horizon decision-making tasks into simpler subtasks.
