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Updated: May 10, 2025
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Using Neuron Spiking Activity to Trigger Closed-Loop Stimuli in Neurophysiological Experiments
Published on: November 12, 2019
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Toward a Biologically Plausible SNN-Based Associative Memory with Context-Dependent Hebbian Connectivity.
S Yu Makovkin1, S Yu Gordleeva2,3,4, I A Kastalskiy5,6
1Department of Applied Mathematics, Institute of Information Technology, Mathematics and Mechanics, Lobachevsky State University of Nizhny Novgorod, 23 Gagarin Avenue, Nizhny Novgorod 603022, Russia.
International Journal of Neural Systems
|April 20, 2025
Summary
We developed an energy-efficient spiking neural network for associative memory using Hebbian learning. This model uses synchronized neuron oscillations to recognize binary images, paving the way for advanced AI hardware.
Area of Science:
- Computational Neuroscience
- Artificial Intelligence
- Neurocomputing
Background:
- Associative memory is crucial for cognitive functions.
- Existing models often lack energy efficiency.
- Spiking neural networks offer a biologically plausible and potentially efficient alternative.
Purpose of the Study:
- To propose a novel spiking neural network model for energy-efficient associative memory.
- To implement Hebbian learning for information storage and retrieval.
- To explore context-dependent signal processing for pattern recognition.
Main Methods:
- A three-layer spiking neural network using Hodgkin-Huxley-Mainen neurons.
- Hebbian learning implemented via a symmetric connectivity matrix.
- Binary image encoding using in-phase/anti-phase oscillations and phase-locking for synchronization.
- Interneurons for context-dependent filtering of synaptic pathways.
Main Results:
- Demonstrated information pattern retrieval via stimulus response.
- Achieved cluster synchronization in input and output layers through phase-locking.
- Showcased context-dependent engagement of synaptic connections for recognition.
- Investigated oscillation phase stability for direct and inverse image recognition.
Conclusions:
- The proposed model offers an energy-efficient approach to associative memory.
- Context-dependent processing enhances recognition capabilities.
- The model shows potential for analog hardware implementation in neurocomputing and AI.
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