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Updated: May 9, 2026
11:18
Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
Published on: March 2, 2015
Synthesizing cognition in neuromorphic electronic systems
Emre Neftci1, Jonathan Binas, Ueli Rutishauser
1Institute of Neuroinformatics, University of Zurich and Eidgenössiche Technische Hochschule Zurich, 8057 Zurich, Switzerland.
Summary
Researchers developed a new method for reliable intelligent processing in neuromorphic systems. This approach maps imprecise hardware neurons to reliable computational subnetworks, enabling robust behavioral dynamics for tasks like motion pattern classification.
Area of Science:
- Neuroscience
- Computer Science
- Electrical Engineering
Background:
- Implementing intelligent processing in electronic neuromorphic systems is challenging due to the inherent imprecision and noise of hardware neurons.
- Existing methods struggle to achieve reliable behavioral dynamics on these unreliable substrates.
Purpose of the Study:
- To present a novel method for achieving reliable behavioral dynamics in electronic neuromorphic systems.
- To enable robust intelligent processing on substrates with imprecise and noisy neurons.
Main Methods:
- Mapping an unreliable hardware layer of spiking silicon neurons to an abstract computational layer of reliable model neuron subnetworks.
- Configuring these subnetworks as generic soft winner-take-all networks for reliable processing.
- Composing target behavioral dynamics as a "soft state machine" on these reliable subnets.
- Calibrating electronic circuit biases against model parameters using population activity measurements.
Main Results:
- Demonstrated a synthesis method for creating reliable neuromorphic systems from imprecise hardware.
- Successfully implemented a neuromorphic sensory agent capable of real-time context-dependent classification of motion patterns.
- The abstract computational layer provides reliable processing through active gain, signal restoration, and multistability.
Conclusions:
- The proposed method offers a viable solution for reliable behavioral dynamics in neuromorphic systems.
- This approach facilitates the implementation of complex intelligent processing on noisy hardware substrates.
- Enables advanced applications such as real-time sensory data classification.
Keywords:
analog very large-scale integrationartificial neural systemsdecision makingsensorimotorworking memoryMore Related Videos
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