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Criticality as a Set-Point for Adaptive Behavior in Neuromorphic Hardware
Narayan Srinivasa1, Nigel D Stepp1, Jose Cruz-Albrecht2
1Information and System Sciences Lab, Center for Neural and Emergent Systems, HRL Laboratories LLC Malibu, CA, USA.
Frontiers in Neuroscience
|December 10, 2015
Summary
Neuromorphic hardware can achieve adaptive behaviors by self-tuning to maintain criticality. This approach enables scalable intelligent systems by embracing complex network dynamics, moving beyond current limitations.
Area of Science:
- Neuroscience and Computer Engineering
- Artificial Intelligence and Machine Learning
Background:
- Current neuromorphic hardware designs often simplify network dynamics, limiting adaptive behavior.
- The brain naturally operates at criticality, exhibiting optimal information processing.
Purpose of the Study:
- To explore how neuromorphic hardware can be designed to self-tune and maintain criticality.
- To enable the development of scalable intelligent systems with emergent adaptive behaviors.
Main Methods:
- Review of existing neuromorphic hardware designs and their limitations.
- Analysis of theoretical and experimental findings on criticality in neuronal networks.
- Argument for self-tuning mechanisms in neuromorphic systems.
Main Results:
- Criticality in neuronal networks is linked to optimal information transfer, learning, and processing.
- Self-tuning to criticality is proposed as a key mechanism for adaptive behavior in neuromorphic systems.
Conclusions:
- Understanding self-tuning to criticality is essential for designing advanced neuromorphic hardware.
- Embracing network complexity, rather than avoiding it, will lead to truly scalable intelligent systems.
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