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Computing of temporal information in spiking neural networks with ReRAM synapses
1Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Piazza L. da Vinci, 32 - 20133 Milano, Italy. daniele.ielmini@polimi.it.
Faraday Discussions
|October 27, 2018
Summary
Resistive switching random-access memory (ReRAM) enables brain-inspired computing. This study demonstrates ReRAM synapses for spiking neural networks (SNNs) that learn and recognize temporal spike sequences, paving the way for efficient neuromorphic systems.
Area of Science:
- Materials Science
- Neuroscience
- Computer Science
Background:
- Resistive switching random-access memory (ReRAM) facilitates resistance switching via ion migration, crucial for artificial synapses in neuromorphic computing.
- Spiking neural networks (SNNs) excel at processing spatiotemporal information, mimicking brain efficiency through precise spike timing.
- Developing comprehensive neuromorphic systems that replicate brain functionality remains a challenge.
Purpose of the Study:
- To develop and implement a neuromorphic SNN system utilizing ReRAM synapses.
- To enable computation of temporal information within neural spikes using spike-timing dependent plasticity (STDP).
- To demonstrate the learning and recognition capabilities of spatiotemporal spike sequences.
Main Methods:
- Experimental implementation of ReRAM-based synapses capable of STDP.
- Development of a neuromorphic SNN system for processing temporal spike patterns.
- Simulation of multi-layer spatiotemporal computing networks.
Main Results:
- Experimental demonstration of learning and recognition for spatiotemporal spike sequences using ReRAM synapses.
- Simulation results confirm the feasibility of constructing multi-layer spatiotemporal computing networks.
- The system shows potential for learning object traces and mimicking biological visual cortex hierarchy.
Conclusions:
- ReRAM synapses are effective for implementing STDP in neuromorphic SNNs.
- The developed system can process and learn spatiotemporal information, crucial for brain-inspired computing.
- This approach offers a pathway towards efficient, hierarchical spatiotemporal computing systems.
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