# full-FORCE: A Target-Based Method for Training Recurrent Networks

**Authors:** Brian DePasquale, Christopher J. Cueva, Kanaka Rajan, G. Sean Escola, L. F. Abbott  
**arXiv:** [1710.03070](https://arxiv.org/abs/1710.03070)  
**Date:** 2017-10-09  
**Summary page:** https://www.summarizepaper.com/en/arxiv-id/1710.03070v1/

## Key points

- Authors introduce a novel approach for training recurrent networks called "full-FORCE"
- Method modifies the full connectivity matrix by introducing a second network during training
- Networks trained using this method require fewer neurons and exhibit greater noise robustness compared to traditional approaches
- Incorporating additional input signals as task hints expands the range of tasks that can be learned
- Enhances complexity and nature of dynamics within the network while providing greater control over performance
- Offers a promising avenue for effectively modeling dynamic neural computations and achieving superior task performance in recurrent networks

## Summary

In their paper titled "full-FORCE: A Target-Based Method for Training Recurrent Networks," authors Brian DePasquale, Christopher J. Cueva, Kanaka Rajan, G. Sean Escola, and L. F. Abbott introduce a novel approach for training recurrent networks to perform tasks involving complex input/output transformations. The proposed method modifies the full connectivity matrix of a recurrent network by introducing a second network during training to provide target dynamics necessary for task performance. This results in networks that require fewer neurons and exhibit greater noise robustness compared to traditional least-squares (FORCE) approaches. The authors also demonstrate how incorporating additional input signals into the target-generating network as task hints significantly expands the range of tasks that can be learned. This not only enhances the complexity and nature of dynamics within the trained network but also provides greater control over its performance. Overall, this innovative target-based training method offers a promising avenue for effectively modeling dynamic neural computations and achieving superior task performance in recurrent networks. The findings presented in this study contribute valuable insights to the field of neural network research and have implications for advancing applications in various domains such as computational neuroscience and machine learning.