Ryan Pyle

TL;DR: In this paper, the dependence of connection probability on distance was incorporated into spiking networks to generate spatiotemporal patterns that can be trained to perform dynamical computations under a reservoir computing framework.

TL;DR: A novel learning algorithm is developed that models the interaction between reinforcement and unsupervised learning observed in experiments and converges on simulated motor tasks on which previous reservoir computing algorithms fail and reproduces experimental findings that relate Parkinson's disease and its treatments to motor learning.

TL;DR: Circuit models with spatio-temporal excitatory and inhibitory interactions generate population variability that captures recorded neuronal activity across cognitive states that provides a critical link between measured cortical circuit structure and recorded population activity.

TL;DR: In this paper, the authors analyze population recordings from the visual pathway where directed attention differentially modulates shared variability within and between areas, which is difficult to explain with externally imposed variability, and show that if the spatial and temporal scales of inhibitory coupling match physiology, network models capture the low dimensional shared variability of population data.