Neuromorphic Cognitive Systems
This research concerns the analysis and development of computational models, hybrid analog/digital VLSI circuits, and multi-chip event-based systems for implementing real-time distributed neural processing systems, and eventually building Neuromorphic Cognitive Systems (i.e. neuromorphic architectures that can learn and reason about the actions to take, in response to the combinations of external stimuli, internal states, and behavioral objectives).
The neuromorphic cognitive systems we develop are aimed at bridging the gap from simple reactive sensory-motor systems to ones that can learn and reason about the actions to take, in response to the combinations of external stimuli, internal states, and behavioral objectives.
These are typically real-time behaving systems comprising multi-chip, multi-purpose spiking neural architectures. They are used to validate brain inspired computational paradigms in real-world scenarios, and to develop a new generation of fault-tolerant event-based computing technologies.
From the engineering and technology point of view, we follow the original definition of neuromorphic engineering: We exploit standard VLSI CMOS technology to build multi-chip spiking neural networks comprising mixed analog/digital circuits that use the physics of silicon to reproduce the biophysics of biological neural processes.
From the theoretical and modeling point of view, we focus our studies on cortical circuits, spike-based learning, decision making and selection mechanisms, soft Winner-Take-All networks, and state-dependent computation.