Fine-Tuning Recurrent Neural Networks
Author Information
Author(s): David MacNeil, Chris Eliasmith
Primary Institution: Centre for Theoretical Neuroscience, University of Waterloo, Waterloo, Canada
Hypothesis
Can a biologically plausible learning rule fine-tune synaptic weights in recurrent neural networks to achieve stability?
Conclusion
The proposed learning rule can effectively fine-tune synaptic weights in recurrent neural networks, achieving stability comparable to or better than traditional methods.
Supporting Evidence
- The learning rule was able to recover from large perturbations of connection weights.
- The model demonstrated robustness to continuous perturbation of connection weights.
- The learning rule allowed the system to recover from the lesioning of cells.
- Results showed that the tuned network can be more stable than the linear optimal case.
- The model's performance compared well with empirical data from goldfish integrators.
Takeaway
This study shows that a new learning rule can help brain-like networks adjust their connections to work better, just like how our brains learn from experience.
Methodology
The study used simulations of a neural integrator model with 40 neurons to test the proposed learning rule under various conditions.
Limitations
The model may not fully capture the biological complexity of real neural systems, and the simulations were based on specific assumptions about neuron properties.
Digital Object Identifier (DOI)
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