STDP as presynaptic activity times rate of change of postsynaptic activity
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Abstract (arXiv)
We introduce a weight update formula that is expressed only in terms of firing rates and their derivatives and that results in changes consistent with those associated with spike-timing dependent plasticity (STDP) rules and biological observations, even though the explicit timing of spikes is not needed. The new rule changes a synaptic weight in proportion to the product of the presynaptic firing rate and the temporal rate of change of activity on the postsynaptic side. These quantities are interesting for studying theoretical explanation for synaptic changes from a machine learning perspective. In particular, if neural dynamics moved neural activity towards reducing some objective function, then this STDP rule would correspond to stochastic gradient descent on that objective function.
Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). First posted on arXiv 19 Sep 2015.
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Citing papers per year: 2021: 1; 2022: 3; 2023: 3; 2024: 0; 2025: 1.
8 papers.
- 2022Scaling Forward Gradient With Local Lossescited by 65
- 2023Brain-Inspired Machine Intelligence: A Survey of Neurobiologically-Plausible Credit Assignmentcited by 13
- 2021BSNN: Towards Faster and Better Conversion of Artificial Neural Networks to Spiking Neural Networks with Bistable Neuronscited by 12
- 2022An STDP-Based Supervised Learning Algorithm for Spiking Neural Networkscited by 3
- 2023Efficient and Flexible Neural Network Training through Layer-wise Feedback Propagationcited by 2
- 2023Spike Accumulation Forwarding for Effective Training of Spiking Neural Networkscited by 1
- 2022aSTDP: A More Biologically Plausible Learningcited by 0
- 2025Biologically Inspired Spiking Diffusion Model with Adaptive Lateral Selection Mechanismcited by 0
- 2025Biologically Inspired Spiking Diffusion Model with Adaptive Lateral Selection Mechanism31 Mar 2025
- 2023Brain-Inspired Machine Intelligence: A Survey of Neurobiologically-Plausible Credit Assignment1 Dec 2023
- 2023Spike Accumulation Forwarding for Effective Training of Spiking Neural Networks4 Oct 2023
- 2023Efficient and Flexible Neural Network Training through Layer-wise Feedback Propagation23 Aug 2023
- 2022Scaling Forward Gradient With Local Losses7 Oct 2022
- 2022aSTDP: A More Biologically Plausible Learning22 May 2022
- 2022An STDP-Based Supervised Learning Algorithm for Spiking Neural Networks7 Mar 2022
- 2021BSNN: Towards Faster and Better Conversion of Artificial Neural Networks to Spiking Neural Networks with Bistable Neurons27 May 2021