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Assembly and Characterization of Biomolecular Memristors Consisting of Ion Channel-doped Lipid Membranes

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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.

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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.