Models of Neural Credit Assignment Mechanisms

学习是日常生活的核心要素。使学习成为可能的神经生理过程被称为突触可塑性。术语突触可塑性包括两个神经元之间通过各自突触的强度进行的通信增强或抑制的所有过程。这些变化可以持续几秒到几分钟的短时间范围，也可以持续几天甚至几年的长时间。突触可塑性的确切机制尚未被揭示。尽管存在大量的实验文献，但关键问题仍无法令人满意地回答：信用或奖励信号如何分配给单个突触，以便神经网络的性能通过这些标记连接的自适应得到增强。本项目将探索在尖峰神经网络中的信用分配问题的可能算法，神经元网络的一个家族，能够模拟大脑中发现的神经元的大多数尖峰行为。学习算法将沿着现有的神经科学实验数据构建，以确保生物可验证性，并提供基于实验验证的模型形成可检验假设的可能性。因此，该项目将有助于更好地了解大脑学习的基本机制。________________________________________

Learning is a core element of the every day life. The underlying neurophysiological processes enable learning are called synaptic plasticity. The term synaptic plasticity incorporates all processes by which the communication between two neurons via the strength of their respective synapse either get enhanced or depressed. These changes can persist for short temporal scopes in the range of seconds to minutes or long durations lasting for days or even years. The exact mechanisms of synaptic plasticity are yet to be uncovered. Even though a huge body of experimental literature exists key questions could not be answered satisfactorily: how does credit or a reward signal gets assigned to single synapses so that the neural networks performance get enhanced by the adaption of these tagged connections.This project is going to explore possible algorithms for the credit assignment problem outlined in the paragraph above in spiking neural networks, a family of neuronal networks able to model the majority of spiking behaviors of neurons found in the brain. The learning algorithms will be constructed along existing experimental data from neuroscience to ensure biological plausibility and offering the possibility to form testable hypothesis based on the model verifiable by experiments. Therefore, the project would contribute to a better understanding of the fundamental mechanisms of learning in the brain. ________________________________________