Neural sampling in a balanced spiking network with internally generated variability
Xinruo Yang, Wenhao Zhang, Brent Doiron · COSYNE 2025
Recent evidence suggests the brain performs sampling-based Bayesian inference to solve various cognitive tasks [1–6]. Neural sampling requires neural circuits to exhibit statistically stable variability to draw posterior samples. While ad-hoc frameworks simply inject randomness into spike emission, it remains unknown how such variability for sampling is generated in biological neural circuits. Here we propose that recurrent spiking circuits can internally generate statistically stable variability for posterior sampling, eliminating the need for an external random source. Our proposed circuit m
ePoster · Neuroscience