Joseph T. Lizier

Inferring Effective Networks of Spiking Neurons Using a Continuous-Time Estimator of Transfer Entropy

This paper introduces a continuous-time estimator of Transfer Entropy for spike trains, demonstrating its effectiveness in inferring effective neuronal networks and outperforming existing methods.

Early lock-in of structured and specialised information flows during neural development

This paper quantifies changes in information flow during neural development using dissociated cell cultures and finds early lock-in of spatial structure and computational roles.

Estimating Transfer Entropy in Continuous Time Between Neural Spike Trains or Other Event-Based Data

This paper introduces a continuous-time transfer entropy estimator for event-based data, showing improved accuracy and efficiency compared to discrete-time methods.

Nonlinear Reconfiguration of Network Edges, Topology and Information Content During an Artifical Learning Task

This study examines how network structure and information content change during an artificial learning task, identifying three phases of network reconfiguration.

Transitions in brain-network level information processing dynamics are driven by alterations in neural gain

This paper shows that changes in neural gain drive transitions between segregated and integrated brain network states, altering information processing dynamics.

Information theoretic evidence for predictive coding in the face processing system

The paper uses information theory to show that prior knowledge about faces is represented in low-frequency brain activity during face detection tasks.