用泊松变分自编码器模拟感知决策的反应时间，更贴近真实神经过程。
Inferring response times of perceptual decisions with Poisson variational autoencoders
- 基于泊松过程建模神经放电，通过变分自编码器学习视觉特征
- 结合贝叶斯解码与熵停止规则，生成符合实验规律的反应时间分布
- 适用于研究感知决策中的速度-准确率权衡，适合神经科学与认知建模者
许多感知决策的特性可由深度神经网络良好建模，但这类架构通常将决策视为瞬时读出，忽略了决策过程的时间动态。本文提出一种图像可计算的感知决策模型，其中选择与反应时间源于对神经放电活动的高效编码和贝叶斯解码。我们使用泊松变分自编码器，从一组速率编码神经元（建模为独立同质泊松过程）中无监督地学习视觉刺激表示。一个任务优化的解码器持续推断动作的近似后验分布，条件于不断输入的放电活动。结合熵基停止规则，该模型可生成试次级的选择与反应时间模式。应用于MNIST数字分类任务时，模型再现了感知决策的关键经验特征，包括随机变异性、右偏的反应时间分布、反应时间随选项数对数增长（希克定律），以及速度-准确率权衡。
原文摘要 · Abstract (English)
Many properties of perceptual decision making are well-modeled by deep neural networks. However, such architectures typically treat decisions as instantaneous readouts, overlooking the temporal dynamics of the decision process. We present an image-computable model of perceptual decision making in which choices and response times arise from efficient sensory encoding and Bayesian decoding of neural spiking activity. We use a Poisson variational autoencoder to learn unsupervised representations of visual stimuli in a population of rate-coded neurons, modeled as independent homogeneous Poisson processes. A task-optimized decoder then continually infers an approximate posterior over actions conditioned on incoming spiking activity. Combining these components with an entropy-based stopping rule yields a principled and image-computable model of perceptual decisions capable of generating trial-by-trial patterns of choices and response times. Applied to MNIST digit classification, the model reproduces key empirical signatures of perceptual decision making, including stochastic variability, right-skewed response time distributions, logarithmic scaling of response times with the number of alternatives (Hick's law), and speed-accuracy trade-offs.
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