We adjust our behavior adaptively, based on experience, to thrive in our environment. Activity-dependent synaptic plasticity within neural circuits is believed to be a fundamental mechanism that enables such adaptive behavior. In this talk, I will introduce a top-down approach to modeling synaptic plasticity. Specifically, recognizing the brain as an information-processing organ, I posit that synaptic plasticity mechanisms have evolved to transmit information across synapses efficiently. It suggests a method to identify hidden independent sources behind sensory scenes. I will demonstrate that it's feasible to reconstruct even nonlinearly mixed sources that underlie sensory inputs when sensors of sufficiently high dimensions are employed. Furthermore, the theory also helps in interpreting experimentally observed results: it reproduces the distinct outcomes of synaptic plasticity observed in up and down states during non-rapid eye movement sleep, shedding light on how memory consolidation might be influenced by the states and spatial scale of slow waves.