eSSENCE@LU 4:1 - Method development for analysis and modelling of large scale electrophysiological recordings using deep artificial neural networks

This projects aims at integrating advanced technologies for: 1) the analyses of recorded brain activity, 2) mathematical image analysis of sensory cues and the outcome of actions and 3) real world applications of artificial intelligence in humanoid robots. The computational platform needed to achieve this will be built on deep artificial neural networks. The project’s two main objectives are the following:

Aim 1 : To develop plausible systems-level network models that can reproduce observed neurophysiological data and generate testable hypotheses about policy/ value functions Method: To extend and connect existing computational models of cortex and basal ganglia and to enhance them with data on network functional connectivity estimated from electrophysiological measurements.

Aim 2: To evaluate action-based learning in autonomous humanoid robots Method: Humanoid robots learn to control an artificial hand in a skilled reaching task by action-based learning and sensory feedback combining systems for intelligent perception with autonomous robotic systems and direct comparisons to brain processes are made.