ArticleFrontiers in neuroscience2018
On Practical Issues for Stochastic STDP Hardware With 1-bit Synaptic Weights.
Article in Frontiers in neuroscience, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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12 citing papers in PubMed.
- Overview of Spiking Neural Network Learning Approaches and Their Computational Complexities.Sensors (Basel, Switzerland) · 2023Review
- A CMOS-memristor hybrid system for implementing stochastic binary spike timing-dependent plasticity.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2022Article
- Application of Neuromorphic Olfactory Approach for High-Accuracy Classification of Malts.Sensors (Basel, Switzerland) · 2022Article
- Stochastic binary synapses having sigmoidal cumulative distribution functions for unsupervised learning with spike timing-dependent plasticity.Scientific reports · 2021Article
- Using a Low-Power Spiking Continuous Time Neuron (SCTN) for Sound Signal Processing.Sensors (Basel, Switzerland) · 2021Article
- Analogue pattern recognition with stochastic switching binary CMOS-integrated memristive devices.Scientific reports · 2020Article
- Article
- Supervised Learning in All FeFET-Based Spiking Neural Network: Opportunities and Challenges.Frontiers in neuroscience · 2020Article
- Neurohybrid Memristive CMOS-Integrated Systems for Biosensors and Neuroprosthetics.Frontiers in neuroscience · 2020Article
- A Hardware-Deployable Neuromorphic Solution for Encoding and Classification of Electronic Nose Data.Sensors (Basel, Switzerland) · 2019Article
- Neuromorphic Spiking Neural Networks and Their Memristor-CMOS Hardware Implementations.Materials (Basel, Switzerland) · 2019Review
- ReStoCNet: Residual Stochastic Binary Convolutional Spiking Neural Network for Memory-Efficient Neuromorphic Computing.Frontiers in neuroscience · 2019Article
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5 authors.
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Abstract
In computational neuroscience, synaptic plasticity learning rules are typically studied using the full 64-bit floating point precision computers provide. However, for dedicated hardware implementations, the precision used not only penalizes directly the required memory resources, but also the computing, communication, and energy resources. When it comes to hardware engineering, a key question is always to find the minimum number of necessary bits to keep the neurocomputational system working satisfactorily. Here we present some techniques and results obtained when limiting synaptic weights to 1-bit precision, applied to a Spike-Timing-Dependent-Plasticity (STDP) learning rule in Spiking Neural Networks (SNN). We first illustrate the 1-bit synapses STDP operation by replicating a classical biological experiment on visual orientation tuning, using a simple four neuron setup. After this, we apply 1-bit STDP learning to the hidden feature extraction layer of a 2-layer system, where for the second (and output) layer we use already reported SNN classifiers. The systems are tested on two spiking datasets: a Dynamic Vision Sensor (DVS) recorded poker card symbols dataset and a Poisson-distributed spike representation MNIST dataset version. Tests are performed using the in-house MegaSim event-driven behavioral simulator and by implementing the systems on FPGA (Field Programmable Gate Array) hardware.
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