ArticleFrontiers in neuroscience2021
Efficient Spike-Driven Learning With Dendritic Event-Based Processing.
Article in Frontiers in neuroscience, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- Learning-efficient spiking neural networks with multi-compartment spatio-temporal backpropagation.iScience · 2025Article
- Temporal dendritic heterogeneity incorporated with spiking neural networks for learning multi-timescale dynamics.Nature communications · 2024Article
- Optimizing event-based neural networks on digital neuromorphic architecture: a comprehensive design space exploration.Frontiers in neuroscience · 2024Article
- Evolving deep convolutional neutral network by hybrid sine-cosine and extreme learning machine for real-time COVID19 diagnosis from X-ray images.Soft computing · 2023Article
- Article
- A 22-pJ/spike 73-Mspikes/s 130k-compartment neural array transceiver with conductance-based synaptic and membrane dynamics.Frontiers in neuroscience · 2023Article
- UAV Localization Algorithm Based on Factor Graph Optimization in Complex Scenes.Sensors (Basel, Switzerland) · 2022Article
- Robust Spike-Based Continual Meta-Learning Improved by Restricted Minimum Error Entropy Criterion.Entropy (Basel, Switzerland) · 2022Article
- Heterogeneous Ensemble-Based Spike-Driven Few-Shot Online Learning.Frontiers in neuroscience · 2022Article
- Rural Workplace Sustainable Development of Smart Rural Governance Workplace Platform for Efficient Enterprise Performances.Journal of environmental and public health · 2022Article
- Outer-synchronization criterions for asymmetric recurrent time-varying neural networks described by differential-algebraic systemFrontiers in computational neuroscience · 2022Article
- Functional Implications of Dale's Law in Balanced Neuronal Network Dynamics and Decision Making.Frontiers in neuroscience · 2022Article
- SAM: A Unified Self-Adaptive Multicompartmental Spiking Neuron Model for Learning With Working Memory.Frontiers in neuroscience · 2022Article
- Real‑time COVID-19 diagnosis from X-Ray images using deep CNN and extreme learning machines stabilized by chimp optimization algorithm.Biomedical signal processing and control · 2021Article
- A Scatter-and-Gather Spiking Convolutional Neural Network on a Reconfigurable Neuromorphic Hardware.Frontiers in neuroscience · 2021Article
- Predicting Spike Features of Hodgkin-Huxley-Type Neurons With Simple Artificial Neural Network.Frontiers in computational neuroscience · 2021Article
- Always-On Sub-Microwatt Spiking Neural Network Based on Spike-Driven Clock- and Power-Gating for an Ultra-Low-Power Intelligent Device.Frontiers in neuroscience · 2021Article
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
A critical challenge in neuromorphic computing is to present computationally efficient algorithms of learning. When implementing gradient-based learning, error information must be routed through the network, such that each neuron knows its contribution to output, and thus how to adjust its weight. This is known as the credit assignment problem. Exactly implementing a solution like backpropagation involves weight sharing, which requires additional bandwidth and computations in a neuromorphic system. Instead, models of learning from neuroscience can provide inspiration for how to communicate error information efficiently, without weight sharing. Here we present a novel dendritic event-based processing (DEP) algorithm, using a two-compartment leaky integrate-and-fire neuron with partially segregated dendrites that effectively solves the credit assignment problem. In order to optimize the proposed algorithm, a dynamic fixed-point representation method and piecewise linear approximation approach are presented, while the synaptic events are binarized during learning. The presented optimization makes the proposed DEP algorithm very suitable for implementation in digital or mixed-signal neuromorphic hardware. The experimental results show that spiking representations can rapidly learn, achieving high performance by using the proposed DEP algorithm. We find the learning capability is affected by the degree of dendritic segregation, and the form of synaptic feedback connections. This study provides a bridge between the biological learning and neuromorphic learning, and is meaningful for the real-time applications in the field of artificial intelligence.
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