Abstract
Neuromorphic hardware are designed by drawing inspiration from biology to overcome limitations of current computer architectures while forging the development of a new class of autonomous systems that can exhibit adaptive behaviors. Several designs in the recent past are capable of emulating large scale networks but avoid complexity in network dynamics by minimizing the number of dynamic variables that are supported and tunable in hardware. We believe that this is due to the lack of a clear understanding of how to design self-tuning complex systems. It has been widely demonstrated that criticality appears to be the default state of the brain and manifests in the form of spontaneous scale-invariant cascades of neural activity. Experiment, theory and recent models have shown that neuronal networks at criticality demonstrate optimal information transfer, learning and information processing capabilities that affect behavior. In this perspective article, we argue that understanding how large scale neuromorphic electronics can be designed to enable emergent adaptive behavior will require an understanding of how networks emulated by such hardware can self-tune local parameters to maintain criticality as a set-point. We believe that such capability will enable the design of truly scalable intelligent systems using neuromorphic hardware that embrace complexity in network dynamics rather than avoiding it.
Highlights
What role does the brain serve for producing adaptive behavior? This intriguing question is a long-standing one
The basic premise for this analogy was that both computers and the brain received information and acted upon it in complex ways to produce an output
This analogy between computers and the brain has provided a candidate mechanism for cognition, equating it with a digital computer program that can manipulate internal representation according to a set of rules
Summary
What role does the brain serve for producing adaptive behavior? This intriguing question is a long-standing one. The basic premise for this analogy was that both computers and the brain received information and acted upon it in complex ways to produce an output This analogy between computers and the brain ( known as the computer metaphor) has provided a candidate mechanism for cognition, equating it with a digital computer program that can manipulate internal representation according to a set of rules. Traditional algorithms that are derived by adopting the computer metaphor have yielded very limited utility in complex, real-world environments, despite several decades of research to develop machines that exhibit adaptive behaviors. A related observation is that the complexity of adaptive behaviors increases with the number of physiological parameters that are to be maintained within their limits We believe that this includes collective essential variables that are learned during the animal’s interaction with the environment.
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
