Online Spatio-Temporal Learning in Deep Neural Networks

Thomas Bohnstingl, Stanislaw Wozniak, Angeliki Pantazi, Evangelos Eleftheriou

Research output: Contribution to journalArticlepeer-review


Biological neural networks are equipped with an inherent capability to continuously adapt through online learning. This aspect remains in stark contrast to learning with error backpropagation through time (BPTT) that involves offline computation of the gradients due to the need to unroll the network through time. Here, we present an alternative online learning algorithm framework for deep recurrent neural networks (RNNs) and spiking neural networks (SNNs), called online spatio-temporal learning (OSTL). It is based on insights from biology and proposes the clear separation of spatial and temporal gradient components. For shallow SNNs, OSTL is gradient equivalent to BPTT enabling for the first time online training of SNNs with BPTT-equivalent gradients. In addition, the proposed formulation unveils a class of SNN architectures trainable online at low time complexity. Moreover, we extend OSTL to a generic form, applicable to a wide range of network architectures, including networks comprising long short-term memory (LSTM) and gated recurrent units (GRUs). We demonstrate the operation of our algorithm framework on various tasks from language modeling to speech recognition and obtain results on par with the BPTT baselines.

Original languageEnglish
JournalIEEE Transactions on Neural Networks and Learning Systems
Publication statusE-pub ahead of print - 2022


  • Approximation algorithms
  • Backpropagation
  • backpropagation through time (BPTT)
  • Biological neural networks
  • Biology
  • Heuristic algorithms
  • Neurons
  • online learning
  • real-time recurrent learning (RTRL)
  • spiking neurons.
  • Training

ASJC Scopus subject areas

  • Software
  • Computer Science Applications
  • Computer Networks and Communications
  • Artificial Intelligence


Dive into the research topics of 'Online Spatio-Temporal Learning in Deep Neural Networks'. Together they form a unique fingerprint.

Cite this