# Supervised Learning in Spiking Neural Networks with FORCE Training

arXiv: https://arxiv.org/abs/1609.02545 · HTML: https://mlsanity.com/paper/1609.02545/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Sep 2016.

## Built on by (most cited first, 13 of 13)

- [A Survey on Reservoir Computing and its Interdisciplinary Applications Beyond Traditional Machine Learning](https://mlsanity.com/paper/2307.15092/) (2023)
- [A Synapse-Threshold Synergistic Learning Approach for Spiking Neural Networks](https://mlsanity.com/paper/2206.06129/) (2022)
- [Supervised training of spiking neural networks for robust deployment on mixed-signal neuromorphic processors](https://mlsanity.com/paper/2102.06408/) (2021)
- [Learning Reservoir Dynamics with Temporal Self-Modulation](https://mlsanity.com/paper/2301.09235/) (2023)
- [Beyond accuracy: generalization properties of bio-plausible temporal credit assignment rules](https://mlsanity.com/paper/2206.00823/) (2022)
- [Reservoir Computing Benchmarks: a tutorial review and critique](https://mlsanity.com/paper/2405.06561/) (2024)
- [Learning in Feedback-driven Recurrent Spiking Neural Networks using full-FORCE Training](https://mlsanity.com/paper/2205.13585/) (2022)
- [Composite FORCE learning of chaotic echo state networks for time-series prediction](https://mlsanity.com/paper/2207.02420/) (2022)
- [Emergent Computations in Trained Artificial Neural Networks and Real Brains](https://mlsanity.com/paper/2212.04938/) (2022)
- [Neuromorphic Auditory Perception by Neural Spiketrum](https://mlsanity.com/paper/2309.05430/) (2023)
- [Spiking mode-based neural networks](https://mlsanity.com/paper/2310.14621/) (2023)
- [ELiSe: Efficient Learning of Sequences in Structured Recurrent Networks](https://mlsanity.com/paper/2402.16763/) (2024)
- [STAER: Temporal Aligned Rehearsal for Continual Spiking Neural Network](https://mlsanity.com/paper/2601.20870/) (2026)

## Abstract (arXiv)

Populations of neurons display an extraordinary diversity in the behaviors they affect and display. Machine learning techniques have recently emerged that allow us to create networks of model neurons that display behaviours of similar complexity. Here, we demonstrate the direct applicability of one such technique, the FORCE method, to spiking neural networks. We train these networks to mimic dynamical systems, classify inputs, and store discrete sequences that correspond to the notes of a song. Finally, we use FORCE training to create two biologically motivated model circuits. One is inspired by the zebra-finch and successfully reproduces songbird singing. The second network is motivated by the hippocampus and is trained to store and replay a movie scene. FORCE trained networks reproduce behaviors comparable in complexity to their inspired circuits and yield information not easily obtainable with other techniques such as behavioral responses to pharmacological manipulations and spike timing statistics.