Neural sampling

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

…to what degree they can also explain neural activity is still an open question. While decoding approaches that link neural variability to behavioral uncertainty provide some evidence, stronger tests have tried to link posterior beliefs about specific latent variables in a generative model to neural responses. On one hand, the specificity of the resulting predictions is desirable since it allows us to decide which of the infinitely many parameterizations of the task model (ideal observer) is more closely align