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Generation of Local CA1 γ Oscillations by Tetanic Stimulation

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Stochastic transition in synchronized spiking nanooscillators.

Erbin Qiu^1,2, Pavel Salev³, Felipe Torres⁴

¹Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, CA 92093.

Proceedings of the National Academy of Sciences of the United States of America

|September 11, 2023

Summary

Synchronization of nanoscale spiking oscillators differs from harmonic oscillators. A stochastic regime, not loss of coherence, governs transitions between 1:1 and 2:1 modes, crucial for neuromorphic computing.

Keywords:

spiking oscillators stochastic transition synchronization evolution thermal coupling time domain phase coexistence

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Area of Science:

Condensed Matter Physics
Nonlinear Dynamics
Nanotechnology

Background:

Conventional harmonic oscillators exhibit predictable synchronization.
Mott materials offer unique properties for nanoscale electronic devices.
Coupled nanoscale oscillators are key for advanced computing architectures.

Purpose of the Study:

Investigate synchronization dynamics in Mott material-based spiking nanooscillators.
Characterize the role of thermal interactions in oscillator coupling.
Explore novel synchronization regimes and their implications for computational paradigms.

Main Methods:

Fabrication of coupled Mott material-based spiking nanooscillators.
Experimental control of driving voltage to influence oscillator behavior.
Analysis of synchronization modes (1:1 and 2:1) and transition dynamics.
Characterization of thermal interaction effects.

Main Results:

Synchronization of spiking nanooscillators is distinct from harmonic oscillators.
Thermal interactions significantly mediate synchronization between closely spaced devices.
Integer synchronization modes (1:1 and 2:1) are controllable via driving voltage.
A stochastic synchronization regime, not loss of coherence, governs mode transitions.

Conclusions:

The stochastic synchronization regime is a critical factor in spiking nanooscillator networks.
Understanding this regime is essential for designing large-scale neuromorphic and stochastic computing systems.
Mott material-based oscillators present a promising platform for novel computational hardware.