Обучение с подкреплением спайковнейронной сети в задаче управления агентомой в дискретной виртуальной среде

В работе описываются методы обучения с подкреплением спайковой нейронной сети, управляющей роботом или интеллектуальным агентом. Применение спайковых нейронов в качестве базовых элементов сети позволяет использовать как пространственную, так и временную структуру входной сенсорной информации. Обучение сети производится с помощью подкрепляющих сигналов, идущих из внешней среды и отражающих степень успешности недавно выполненных агентом действий. Максимизация получаемого подкрепления ведется путем модулированной минимизации информационной энтропии функционирования нейрона, которая зависит от весов нейронов. Полученные законы изменения весов близки к явлениям синаптической пластичности, наблюдающейся в реальных нейронах. Работа алгоритма обучения с подкреплением проверяется на тестовой задаче поиска ресурсов агентом в дискретной виртуальной среде

Computational modeling with spiking neural networks

This chapter reviews recent developments in the area of spiking neural networks (SNN) and summarizes the main contributions to this research field. We give background information about the functioning of biological neurons, discuss the most important mathematical neural models along with neural encoding techniques, learning algorithms, and applications of spiking neurons. As a specific application, the functioning of the evolving spiking neural network (eSNN) classification method is presented in detail and the principles of numerous eSNN based applications are highlighted and discussed

Learning in large-scale spiking neural networks

Learning is central to the exploration of intelligence. Psychology and machine learning provide high-level explanations of how rational agents learn. Neuroscience provides low-level descriptions of how the brain changes as a result of learning. This thesis attempts to bridge the gap between these two levels of description by solving problems using machine learning ideas, implemented in biologically plausible spiking neural networks with experimentally supported learning rules. We present three novel neural models that contribute to the understanding of how the brain might solve the three main problems posed by machine learning: supervised learning, in which the rational agent has a fine-grained feedback signal, reinforcement learning, in which the agent gets sparse feedback, and unsupervised learning, in which the agents has no explicit environmental feedback. In supervised learning, we argue that previous models of supervised learning in spiking neural networks solve a problem that is less general than the supervised learning problem posed by machine learning. We use an existing learning rule to solve the general supervised learning problem with a spiking neural network. We show that the learning rule can be mapped onto the well-known backpropagation rule used in artificial neural networks. In reinforcement learning, we augment an existing model of the basal ganglia to implement a simple actor-critic model that has a direct mapping to brain areas. The model is used to recreate behavioural and neural results from an experimental study of rats performing a simple reinforcement learning task. In unsupervised learning, we show that the BCM rule, a common learning rule used in unsupervised learning with rate-based neurons, can be adapted to a spiking neural network. We recreate the effects of STDP, a learning rule with strict time dependencies, using BCM, which does not explicitly remember the times of previous spikes. The simulations suggest that BCM is a more general rule than STDP. Finally, we propose a novel learning rule that can be used in all three of these simulations. The existence of such a rule suggests that the three types of learning examined separately in machine learning may not be implemented with separate processes in the brain

A reinforcement learning algorithm for spiking neural networks, in

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