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Modeling homosynaptic and heterosynaptic plasticity with a single neuromemristive synapse
Zubaer Ibna Mannan1, Sami Azam2, Ram Kaji Budhathoki3
1Department of Computer Science and Engineering, East West University, Jahurul Islam Ave., Dhaka 1212, Bangladesh.
Journal of Advanced Research
|October 24, 2025
Summary
This study introduces a novel neuromemristive synapse that accurately mimics brain-like plasticity, including homosynaptic and heterosynaptic processes. This advancement paves the way for more sophisticated artificial intelligence and neuromorphic computing systems.
Area of Science:
- Neuroscience
- Materials Science
- Computer Engineering
Background:
- Current artificial intelligence struggles to replicate the brain's robustness and learning capabilities.
- Existing bioelectronic devices have limited ability to emulate complex synaptic plasticity like homosynaptic and heterosynaptic processes.
Purpose of the Study:
- To develop a neuromemristive synapse capable of emulating key biological features, including homosynaptic and heterosynaptic plasticity.
- To leverage memristor technology for bio-realistic synaptic emulation with low power and CMOS compatibility.
Main Methods:
- Designed a composite 1-port artificial synapse using a memristor and a controlled capacitor.
- Simulated synaptic plasticity using distinct brainwave patterns and modulated discharge rates via the capacitor for potentiation and depression.
Main Results:
- Validated the synapse's bio-functionalities through SPICE simulations, demonstrating low power consumption.
- Successfully replicated neurobiological processes: heterosynaptic homeostasis, modular input specificity, associativity, homosynaptic long-term/short-term potentiation and depression (LTP, LTD, STF, STD), memory fading effect (MFE), and strong stimulation (SST).
Conclusions:
- The proposed synapse bio-realistically mimics synaptic plasticity and neuronal responses.
- Implemented with off-the-shelf components, it supports volatile/non-volatile modes for CMOS integration.
- Enables advancements in spiking neural networks, brain function analysis, and scalable neuromorphic computing.
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