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Spiking Neural Networks Based on OxRAM Synapses for Real-Time Unsupervised Spike Sorting
Thilo Werner1, Elisa Vianello1, Olivier Bichler2
1Laboratoire d'Électronique et de Technologie de l'Information (LETI), Commissariat à l'Énergie Atomique et aux Énergies Alternatives (CEA)Grenoble, France; Université Grenoble AlpesGrenoble, France.
Frontiers in Neuroscience
|November 19, 2016
Summary
This study introduces a novel spiking neural network (SNN) using RRAM technology for real-time spike sorting. This approach offers low power consumption and high accuracy for brain-computer interfaces and neural prosthetics.
Area of Science:
- Neuroscience
- Materials Science
- Computer Engineering
Background:
- Spike sorting is crucial for analyzing complex neural signals.
- Conventional methods face limitations in real-time processing and power efficiency.
- Brain-computer interfaces (BCI) and neural prosthetics require advanced signal processing techniques.
Purpose of the Study:
- To present a novel spiking neural network (SNN) architecture for efficient spike sorting.
- To leverage resistive random-access memory (RRAM) technology for synapse implementation.
- To demonstrate the potential for real-time, low-power neural signal processing.
Main Methods:
- Developed an SNN architecture utilizing Oxide RRAM (OxRAM) for synapses.
- Employed an online learning strategy inspired by Spike Timing Dependent Plasticity.
- Validated the SNN using intra- and extracellularly recorded spiking data from a Crayfish sensory-motor system.
Main Results:
- Achieved low-latency (<1μs) and ultra-low power (nW range) spike sorting.
- Demonstrated RRAM synapses with low energy consumption (<75 pJ) and ease of programming.
- The OxRAM-based SNN achieved approximately 90% recognition rate for different spike shapes without supervision.
Conclusions:
- The proposed OxRAM-based SNN offers a promising alternative for real-time spike sorting.
- This technology enables potential advancements in BCI, neural prosthetics, and autonomous implantable devices.
- The system demonstrates effective unsupervised learning and recognition of neural signals.
