HBP Tea and Slides

Brain-inspired control systems

Jens E. Pedersen



... how we can train brain-inspired control systems on neuromorphic hardware

... by exploiting the growing knowledge within both neuroscience and computer science

  1. The NeuroComputing Systems lab at KTH
  2. Algorithms and brains
  3. Robots and brains
  4. What happens next?

1. The NeuroComputing Systems (NCS) lab at KTH

Discovering key principles by which brains work and implementing them in technical systems that intelligently interact with their environment.


  • Analog VLSI Circuits to implement neural processing
  • Sensors, such as Silicon Retinae or Cochleae
  • Distributed Adaptive Control
  • Massively Parallel Self-Growing Computing Architectures
  • Neuro-Electronic Implants, Rehabilitation

2. Algorithms and brains

2.1 Algorithms for computers

Source: Wikipedia

$\models$ Algorithms are unambiguous

2.2 Algorithms for the real world

Source: Harry Potter and the Sorcerer's Stone

2.3 Algorithms for organisms

Source: David Rogers, Vanderbilt University

2.4 Algorithm in the brain

  • Modern neuroscience pinpoints specific tasks and their (partial) solutions in the brains

DAC-h3: A Proactive Robot Cognitive Architecture to Acquire and Express Knowledge About the World and the Self

3. Soft robotics and neuromorphic computing

3.1 Algorithms for robots

Source: Makerbot, Financial Times
Source: DARPA robotics challenge 2015

3.2 Brain-inspired controller for the Baxter robot

Source: https://ieeexplore.ieee.org/document/8880621
Source: Baxter

3.3 Myorobotic arm

Source: NCS, KTH, https://ieeexplore.ieee.org/document/7553551
Source: NCS, KTH, https://ieeexplore.ieee.org/document/7553551

4. What's next? Integrating neuroscience and neurorobotics

  • Energy-efficient, scalable algorithms and hardware
    • to implement neuronal algorithms in real-time
    • to interpret (neuronal) sensory input in real-time
  • Event-based sensors
    • Dynamic vision systems (DVS)
    • Neuro-prosthetics
  • Neuronal recordings (EMG / EEG) to create motor signals in real-time
  • Robotic interaction within their environment

HBP WP3: Adaptive networks for cognitive architectures: from advanced learning to neurorobotics and neuromorphic applications

WPO3.1: Enhanced real-world task performance through biologically plausible adaptive cognitive architectures running on neuromorphic hardware and closed-loop neuro-robotics platform

HBP Tea and Slides

Brain-inspired control systems

Jens E. Pedersen