arXiv:2602.03896v2 Announce Type: replace-cross Abstract: Poisson-distributed latent variable models are widely used in computational neuroscience, but differentiating through discrete stochastic samples remains challenging. Two approaches address this: *Exponential Arrival Time* (EAT) simulation and *Gumbel-SoftMax* (GSM) relaxation. We provide the first systematic comparison of these methods, along with practical guidance for practitioners. Our main technical contribution is a modification to the EAT method that theoretically guarantees an unbiased first moment (exactly matching the firing r
The continuous drive for more efficient and accurate AI models, particularly in biological and neuro-inspired computing, necessitates improved gradient estimation techniques for discrete stochastic processes.
This research provides practical guidance and technical improvements for a foundational challenge in AI and computational neuroscience, potentially accelerating progress in models dealing with discrete events, like neural spike trains.
A more reliable and unbiased method for gradient estimation in Poisson-distributed latent variable models becomes available, which can lead to better training outcomes and understanding of such systems.
- · AI researchers (reinforcement learning)
- · Computational neuroscientists
- · Developers of discrete stochastic models
- · Biotech and pharma (AI-driven drug discovery)
- · Inefficient or less accurate gradient estimation methods
Improved models for neural activity and other discrete event systems due to more effective training.
Faster development and deployment of AI systems tailored for biological or event-based data.
Enhanced AI capabilities in areas like brain-computer interfaces or drug discovery where precise modeling of discrete events is critical.
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Read at arXiv cs.LG