A system configured to deliver sensory stimulation to a user during a sleep session, the system comprising: one or more sensors configured to generate output signals conveying information related to brain activity of the user during the sleep session; one or more sensory stimulators configured to provide the sensory stimulation to the user during the sleep session; and one or more hardware processors coupled to the one or more sensors and the one or more sensory stimulators, the one or more hardware processors configured by machine-readable instructions to: obtain historical sleep depth information for a population of users, the historical sleep depth information being related to brain activity of the population of users that indicates sleep depth over time during sleep sessions of the population of users; cause a neural network to be trained based on the historical sleep depth information by providing the historical sleep depth information as input to the neural network; cause, based on the output signals, the trained neural network to predict future times during the sleep session at which the user will be in a deep sleep stage, the trained neural network comprising an input layer, an output layer, and one or more intermediate layers between the input layer and the output layer; determine, with respect to each of the future times, one or more values generated by the one or more intermediate layers of the trained neural network; and characterised in that the one or more hardware processors are further configured to cause the one or more sensory stimulators to provide the sensory stimulation to the user at the future times and to modulate a timing and/or intensity of the sensory stimulation during the sleep session based on the one or more values of the one or more intermediate layers. one or more sensors configured to generate output signals conveying information related to brain activity of the user during the sleep session; one or more sensory stimulators configured to provide the sensory stimulation to the user during the sleep session; and one or more hardware processors coupled to the one or more sensors and the one or more sensory stimulators, the one or more hardware processors configured by machine-readable instructions to: obtain historical sleep depth information for a population of users, the historical sleep depth information being related to brain activity of the population of users that indicates sleep depth over time during sleep sessions of the population of users; cause a neural network to be trained based on the historical sleep depth information by providing the historical sleep depth information as input to the neural network; cause, based on the output signals, the trained neural network to predict future times during the sleep session at which the user will be in a deep sleep stage, the trained neural network comprising an input layer, an output layer, and one or more intermediate layers between the input layer and the output layer; determine, with respect to each of the future times, one or more values generated by the one or more intermediate layers of the trained neural network; and characterised in that the one or more hardware processors are further configured to cause the one or more sensory stimulators to provide the sensory stimulation to the user at the future times and to modulate a timing and/or intensity of the sensory stimulation during the sleep session based on the one or more values of the one or more intermediate layers. obtain historical sleep depth information for a population of users, the historical sleep depth information being related to brain activity of the population of users that indicates sleep depth over time during sleep sessions of the population of users; cause a neural network to be trained based on the historical sleep depth information by providing the historical sleep depth information as input to the neural network; cause, based on the output signals, the trained neural network to predict future times during the sleep session at which the user will be in a deep sleep stage, the trained neural network comprising an input layer, an output layer, and one or more intermediate layers between the input layer and the output layer; determine, with respect to each of the future times, one or more values generated by the one or more intermediate layers of the trained neural network; and characterised in that the one or more hardware processors are further configured to cause the one or more sensory stimulators to provide the sensory stimulation to the user at the future times and to modulate a timing and/or intensity of the sensory stimulation during the sleep session based on the one or more values of the one or more intermediate layers.