  The brain can learn to execute a wide variety of tasks quickly and
efficiently. Nevertheless, most of the mechanisms that enable us to learn are
unclear or incredibly complicated. Recently, considerable efforts have been
made in neuroscience and artificial intelligence to understand and model the
structure and mechanisms behind the amazing learning capability of the brain.
However, in the current understanding of cognitive neuroscience, it is widely
accepted that synaptic plasticity plays an essential role in our amazing
learning capability. This mechanism is also known as the Credit Assignment
Problem (CAP) and is a fundamental challenge in neuroscience and Artificial
Intelligence (AI). The observations of neuroscientists clearly confirm the role
of two important mechanisms including the error feedback system and
unsupervised learning in synaptic plasticity. With this inspiration, a new
learning rule is proposed via the fusion of reinforcement learning (RL) and
unsupervised learning (UL). In the proposed computational model, the nonlinear
optimal control theory is used to resemble the error feedback loop systems and
project the output error to neurons membrane potential (neurons state), and an
unsupervised learning rule based on neurons membrane potential or neurons
activity are utilized to simulate synaptic plasticity dynamics to ensure that
the output error is minimized.
