Learning to acquire novel cognitive tasks with evolution, plasticity and meta-meta-learning

In one In meta-learning, networks are trained with external method, the "inner loop" stores information in the algorithms to learn tasks that require acquiring, time-varying activities of a recurrent network, which storing and exploiting unpredictable information for is slowly optimized in the "outer loop" over many each new instance of the task. However, animals are episodes [Hochreiter et al., 2001, Wang et al., 2016, able to pick up such cognitive tasks automatically, Duan et al., 2016]. A biological interpretation of as a result of their evolved neural architecture and this method is that the inner loop represents the synaptic plasticity mechanisms. Here we evolve neural within-episode self-sustaining activity of cerebral cortex, networks, endowed with plastic connections, over while the outer loop represents lifetime sculpting a sizeable set of simple meta-learning tasks based on of neural connections by value-based neural plasticity a framework from computational neuroscience. The (this interpretation is explored in detail by Wang resulting evolved network can automatically acquire et al. [2018]).