Topic: vector field

Learning-to-learn, a progressive acceleration of learning while solving a series of similar problems, represents a core process of knowledge acquisition. To investigate its underlying brain mechanism, we trained a recurrent neural network (RNN) model on a series of arbitrary sensorimotor mapping problems. The network displayed an exponential speedup in learning across problems. The neural substrate of a sensorimotor task schema emerges as low-dimensional neural representations of task variables that are shared across problems. Its reuse limits the connection weight changes required to learn ne

Observations that have received little attention indicate that complexity is important for flexibility and adaptability of natural behavior and the remarkably efficient learning of the brain. The term complexity implies meaningful structural richness, which exists in systems that offer a plurality of possible solutions. The existence of multiple solutions in the neuronal network enables the brain to interpret a diverse set of sensory inputs and can allow it to operate and respond flexibly across a wide array of contexts. Hence it is important to study factors that can enable network complexity