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Published on: May 30, 2014
Noise-constrained switching times for heteroclinic computing
Fabio Schittler Neves1, Maximilian Voit1, Marc Timme1
1Network Dynamics, Max Planck Institute for Dynamics and Self-Organization, 37077 Göttingen, Germany.
Chaos (Woodbury, N.Y.)
|April 3, 2017
Summary
Heteroclinic computing uses dynamic networks for computation. This study reveals how noise and system dynamics affect switching times in pulse-coupled systems, crucial for reliable analog computation under natural conditions.
Area of Science:
- Computational neuroscience
- Complex systems dynamics
- Analog computing
Background:
- Heteroclinic computing leverages collective system dynamics for universal computation.
- Input signals are encoded as orbits approaching saddle states within heteroclinic networks.
- Pulse-coupled oscillators and spiking neurons naturally form these networks, offering a substrate for analog computation.
Purpose of the Study:
- To systematically investigate switching times in pulse-coupled systems.
- To understand the influence of noise and intrinsic dissipation on switching times.
- To explore the relationship between switching times, computation reliability, and signal intensity coding.
Main Methods:
- Systematic investigation of switching times in dependence of noise and intrinsic dissipation levels.
- Analysis of the interplay between local pulse responses and external noise.
- Characterization of switching times and computation reliability.
Main Results:
- Switching times in pulse-coupled systems increase exponentially with the number of switches, influenced by noise levels.
- Switching times serve as a predictor for computation reliability.
- Identified a complementary coding scheme for signal intensity, moving beyond signal identity coding.
Conclusions:
- Findings provide insights into heteroclinic computing under noisy, natural conditions.
- Switching time characterization is key for designing reliable heteroclinic computing hardware.
- Results pave the way for coding signal intensity in heteroclinic computing systems.
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