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Ankur Bhaumik¹, Uddipan Ghosh¹, Hridesh Kumar Gupta¹

¹Solid State and Structural Chemistry Unit, Indian Institute of Science, Bangalore, Karnataka, India. anshup@iisc.ac.in.

Materials Horizons

|May 22, 2026

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Researchers developed a novel oxide nanoparticle system that mimics global brain dynamics like neuronal oscillations and memory. This brain-inspired hardware offers a new path toward efficient computing by emulating collective brain functions.

Area of Science:

Neuroscience
Materials Science
Computer Engineering

Background:

Conventional computing struggles with power efficiency and mimicking global brain functions like synchronization and memory.
Existing brain-inspired hardware often focuses on local neural features (spiking, plasticity) but lacks global dynamics emulation.

Purpose of the Study:

To demonstrate a non-memristive material system capable of emulating collective neuronal oscillations and memory.
To engineer a system that replicates global brain dynamics using principles of excitatory and inhibitory neuronal interactions.

Main Methods:

Engineered an oxide nanoparticle system exhibiting spontaneous current oscillations under DC bias.
Analyzed oscillation characteristics (spectral entropy) at varying electrode separations.

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Investigated system response to external stimuli to demonstrate memory effects.

Utilized Kinetic Monte Carlo simulations to model and validate experimental dynamics.

Main Results:

The oxide nanoparticle system showed spontaneous current oscillations with low spectral entropy, mimicking coherent neuronal rhythms.
Observed scale-dependent synchronization, with random fluctuations at small separations and coherent oscillations at larger ones.
Demonstrated stimulation-history-dependent memory by reconfiguring oscillatory dynamics in response to stimuli.
Simulations confirmed collective synchronization and reproduced experimental dynamics.

Conclusions:

A novel non-memristive material system successfully emulates global brain dynamics, specifically collective neuronal oscillations and memory.
The engineered system provides a proof-of-principle for brain-inspired hardware that goes beyond local neural features.
This work opens new avenues for developing power-efficient computing hardware inspired by complex brain functions.