Topic: locally amplifying network motifs

Complex movements, such as riding a bike, require rich, coordinated, self-sustaining patterns of neural activity. Experimental studies show that such activity patterns are often controlled by brief, intermittent inputs. In contrast, computational models usually rely on constant external inputs or generate only transient --ballistic-- responses in networks tuned for that purpose. Here, we propose a novel approach that selects for locally amplifying network motifs to generate a diverse repertoire of dynamic behaviors without continuous external stimulation. Brief inputs to these models can induc