On the spatiotemporal behavior in biology-mimicking computing systems
J\'anos V\'egh, \'Ad\'am J. Berki

TL;DR
This paper explores the importance of spatiotemporal dynamics in biology-mimicking computing systems, proposing a temporal logic framework to better understand and emulate biological neural behavior in artificial systems.
Contribution
It introduces a Minkowski transform-based temporal logic to analyze and explain the spatiotemporal behavior in biological and neuromorphic computing systems, addressing a key gap in current models.
Findings
Temporal behavior is crucial for accurate biological neural system imitation.
The Minkowski transform provides a quantitative tool for analyzing spatiotemporal dynamics.
Understanding temporal effects can improve the design and performance of neuromorphic systems.
Abstract
The payload performance of conventional computing systems, from single processors to supercomputers, reached its limits the nature enables. Both the growing demand to cope with "big data" (based on, or assisted by, artificial intelligence) and the interest in understanding the operation of our brain more completely, stimulated the efforts to build biology-mimicking computing systems from inexpensive conventional components and build different ("neuromorphic") computing systems. On one side, those systems require an unusually large number of processors, which introduces performance limitations and nonlinear scaling. On the other side, the neuronal operation drastically differs from the conventional workloads. The conventional computing (including both its mathematical background and physical implementation) is based on assuming instant interaction, while the biological neuronal systems…
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Taxonomy
TopicsNeural Networks and Applications · Neural dynamics and brain function
