Neural spiking for causal inference and learning

doi:10.1371/journal.pcbi.1011005

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NeuroAI · 2023-01-01

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1. Part 1
2. Part 2
3. Part 3
4. Part 4
5. Part 5

Contents

• Neural spiking for causal inference and learning
• Neural spiking for causal inference and learning
• Figures
• Abstract
• Author summary
• Introduction
• Results
• Causal inference in neural networks
• Result I.
• From dynamic neural networks to probabilistic graphical models.
• A causal Bayesian network of a neural network, and a neuron’s causal effect on reward.
• Result II: Causal effects and finite difference approximation of gradients.
• Estimating a neuron’s causal effect using the spiking discontinuity
• A simple empirical demonstration of SDE
• Learning with a discontinuity-based causal effect estimator
• The impact of network depth and width on causal effect estimation
• Discussion
• Compatibility with known physiology
• Conclusion
• Methods
• Neuron simulations and noise
• Reward model and causal effect estimation
• Learning causal effects through the spiking discontinuity
• The causal effect as a finite-difference operator
• Wider and deeper network implementations
• Supporting information
• S1 Text. Delayed XOR task description, methods and results.
• S1 Fig. Error in causal effect estimation for LIF networks with different refractory periods.
• S2 Fig. One-sided discontinuity estimator.
• S3 Fig. Learning delayed XOR in the presence of correlated noise.
• Acknowledgments