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Updated: Jul 1, 2025
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Assembly and Characterization of Biomolecular Memristors Consisting of Ion Channel-doped Lipid Membranes
Published on: March 9, 2019
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Fractional order memcapacitive neuromorphic elements reproduce and predict neuronal function.
Patricia Vazquez-Guerrero1, Rohisha Tuladhar1, Costas Psychalinos2
1Department of Neuroscience, Developmental and Regenerative Biology, The University of Texas at San Antonio, San Antonio, TX, 78349, USA.
Scientific Reports
|March 9, 2024
Summary
Fractional order memcapacitors, implemented using super-capacitors, enable efficient neuromorphic systems with history-dependent neuronal functions. These memcapacitors mimic biological neurons, showing power-law adaptation and criticality for advanced computing.
Area of Science:
- Neuroscience
- Electrical Engineering
- Computational Science
Background:
- Neuromorphic systems require energy and computational efficiency.
- Memelectronic elements (mems) can intrinsically implement neuronal functions.
- History-dependent spike time adaptation is crucial for biological neurons but underutilized in neuromorphic systems.
Purpose of the Study:
- To implement fractional order spiking neurons using super-capacitors.
- To investigate power-law spiking time adaptation and optimal coding properties in these circuits.
- To explore the potential of fractional order memcapacitors for efficient neuromorphic computing.
Main Methods:
- Implemented fractional order leaky integrate-and-fire and Hodgkin-Huxley neuron circuits using super-capacitors.
- Characterized the spiking dynamics and adaptation properties of the implemented circuits.
- Compared circuit dynamics with experimental recordings from weakly-electric fish neurons.
Main Results:
- Super-capacitor based circuits exhibited power-law spiking time adaptation and optimal coding.
- The fractional order Hodgkin-Huxley circuit demonstrated novel dynamics consistent with criticality.
- Circuit dynamics were validated against live fish recordings, confirming criticality and predicting experimental results.
Conclusions:
- Fractional order memcapacitors offer intrinsic memory dependence for computationally efficient neuromorphic devices.
- Memcapacitors provide a low-energy alternative to memristors for energetically efficient neuromorphic hardware.
- This work bridges theoretical models of neuronal adaptation with practical hardware implementation.
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