← 研踪 PaperTrailarXiv:2611.10359 · v1
Spiking Sensory Networks
首次提交 2026-11-07 08:44
版本更新 2026-11-07 08:44
雷达观测 2026-11-10 10:11
摘要
Effective sensory reception is critical for survival, yet the computational mechanisms behind how peripheral sensory neurons (SNs) transduce external signals into spiking representations for downstream brain circuits remain to be fully understood. Numerous theoretical frameworks have been proposed to model sensory processing, with Bayesian approaches among the most prominent. However, existing machine learning attempts to apply Bayesian theory to spiking neurons remain largely phenomenological, disconnected from empirical biology, and poorly validated on non-stationary temporal data. Conversely, neuroscientific models often rely on strong inductive experimental biases or explicit kinetic dynamics fit post hoc to match spiking data. Here, we introduce a class of recurrent Poisson VAE models, termed Spiking Sensory Networks (SSNs), that bridge these paradigms by reconciling variational inference with biologically inspired spike transduction for time-varying stimuli. Evaluated on real-world odor plume recordings from locust olfactory receptor neurons (ORNs), SSNs match or exceed the reconstruction fidelity of Spiking Neural Network (SNN) and GLM baselines while exhibiting superior representational characteristics. Furthermore, SSNs are well-suited for neuroscientific investigation, learning empirical in vivo ORN response motifs and exhibiting emergent spike-frequency adaptation under unsupervised reconstruction without manual tuning. Moreover, we theoretically link the recurrent decoding objective to mutual information maximization, and derive a formal thermodynamic lower bound connecting the Poisson Kullback-Leibler regularizer to the excess non-equilibrium metabolic power of biophysical homeostasis. Ultimately, our work synthesizes principles from machine learning theory and empirical neuroscience, providing a viable bridge that concretizes Bayesian models with biological mechanisms.
作者备注：46 pages, 18 figures
观测到的版本
相邻记录可能跳过中间版本；首次发现 v2 及以上也会标为版本更新。
v1 · 2026-11-07 08:44 · 最新观测版本
Spiking Sensory Networks
Effective sensory reception is critical for survival, yet the computational mechanisms behind how peripheral sensory neurons (SNs) transduce external signals into spiking representations for downstream brain circuits remain to be fully understood. Numerous theoretical frameworks have been proposed to model sensory processing, with Bayesian approaches among the most prominent. However, existing machine learning attempts to apply Bayesian theory to spiking neurons remain largely phenomenological, disconnected from empirical biology, and poorly validated on non-stationary temporal data. Conversely, neuroscientific models often rely on strong inductive experimental biases or explicit kinetic dynamics fit post hoc to match spiking data. Here, we introduce a class of recurrent Poisson VAE models, termed Spiking Sensory Networks (SSNs), that bridge these paradigms by reconciling variational inference with biologically inspired spike transduction for time-varying stimuli. Evaluated on real-world odor plume recordings from locust olfactory receptor neurons (ORNs), SSNs match or exceed the reconstruction fidelity of Spiking Neural Network (SNN) and GLM baselines while exhibiting superior representational characteristics. Furthermore, SSNs are well-suited for neuroscientific investigation, learning empirical in vivo ORN response motifs and exhibiting emergent spike-frequency adaptation under unsupervised reconstruction without manual tuning. Moreover, we theoretically link the recurrent decoding objective to mutual information maximization, and derive a formal thermodynamic lower bound connecting the Poisson Kullback-Leibler regularizer to the excess non-equilibrium metabolic power of biophysical homeostasis. Ultimately, our work synthesizes principles from machine learning theory and empirical neuroscience, providing a viable bridge that concretizes Bayesian models with biological mechanisms.
查看此版本解读 →查看此版本原文 ↗