The Neuromorphics Lab has active research in the following areas:

  • Design and implementation of whole-brain systems for virtual and robotic agents (MoNETA class of models)
  • Design and implementation of low-power neuromorphic hardware for autonomous robots
  • Co-design of brain machine interface and adaptive robotics for augmentative robotics, primarily directed towards clinical applications
  • In collaboration with our academic partners, basic research in areas ranging from synaptic plasticity and homeostasis, motor control, vision, among others
  • In collaboration with our industrial partners, technological spin-offs in areas ranging from algorithm design, software, hardware, and robotics

These research areas and projects are organized around a central theme: how can we capture the basic mechanisms of biological intelligence, and translate them in innovative hardware able to control autonomous robot in complex environments. While evolution has provided biological brains with a head start over their silicon counterparts, progress in basic research and emerging computing technologies can help to substantially close this gap, as shown in Figure 3.

Basic neuroscience research studies how brain circuits give rise to behavior (1), and isolates the main computations of these circuits (2). These are translated into mathematical modeling of neurons, networks, and whole brain systems (3). This step paves the way to implementation of these models in software, which can then be run on large clusters of GPUs (4) and simulate virtual agents behaving in virtual environments (5). These models can be then run on a much smaller, denser, portable, and low power hardware (6) that can then power mobile robotic platforms (7).

Figure 3. The NL research area are coordinated around a central goal: building biological-grade intelligent systems and hardware able to control behavior.



The Neuromorphics Lab is highly collaborative with connections across both academia and industry.