Noise activated bistable sensor based on chaotic system with output defined by temporal coding and firing rate

Area of Science:

• Nonlinear Dynamics
• Chaos Theory
• Sensor Technology
• Neuroscience

Background:

• Traditional bistable sensors rely on external bias signals for state transitions.
• Existing noise-activated sensors utilize residence time difference (RTD) for detection.
• Chua's circuit offers a chaotic regime with coexisting attractors for novel sensor applications.

Purpose of the Study:

• To experimentally investigate dc voltage measurements using a noise-driven bistable sensor based on Chua's circuit.
• To quantify sensor output using time proportion in a state and spike-count rate.
• To analyze stimulus-observable relationships, coding schemes, and optimal noise intensity for detection.

Main Methods:

• Utilized an electronic Chua's circuit operating in a chaotic regime with two coexisting single scroll attractors.
• Measured dc voltage output and quantified sensor response by time proportion and spike-count rate.
• Investigated stimulus-observable relationships across varying noise intensities and observation times.

Main Results:

• Established relationships between stimuli and observable outputs for different noise intensities.
• Demonstrated that population coding yields consistent relationships regardless of observation time.
• Identified optimal noise intensity and time windows for detection and population coding.

Conclusions:

• The noise-driven bistable sensor based on Chua's circuit provides a viable method for stimulus detection without external bias.
• Results offer valuable insights for understanding neural activity and designing bistable storage elements in noisy environments.
• Optimal parameters for detection and coding schemes were identified, enhancing sensor performance.