Sampling-Based Probabilistic Inference through Neural and Synaptic Dynamics

Session 1: Models of Probabilistic Inference

What are the theoretical models currently available to implement probabilistic computations in the brain, what are the neural codes for probabilities, uncertainty and related metrics?

Sampling-Based Probabilistic Inference through Neural and Synaptic Dynamics

Session 1: Models of Probabilistic InferenceChair: Wolfgang Maass

Documents et médias

Intervenant(s)

Robert Legenstein

Événements

Opening

On the Adaptive Fitness of the Bayesian Brain

Circular Inference in Schizophrenia

Learning-based cross-modal suppression of ongoing activity in primary cortical areas and the descrip…

Sampling-Based Probabilistic Inference through Neural and Synaptic Dynamics

Brainstem Modulation of Cortical Inference Processes

A Computational Analysis of the Bayesian Brain

Interplay of Synaptic Plasticity and Probabilistic Theories

A Rational Sense of Confidence during Probabilistic Inference in the Human Brain

Empirical Support for Predictive Coding in Sensory Circuits?

Probabilistic Inference and Human Executive Control

Bayesian Accounts of the Central Tendency of Judgment

Hierarchical Predictive Coding and Adaptive Gain Control

Sampling-Based Representation of Uncertainty: Time is of Essence

Gaps between the Observed Stochasticity of Neural Systems and Probabilistic Inference Models

Joining the Dots in theoretical neurobiology

Voir aussi

Session 1: Models of Probabilistic Inference
Chair: Wolfgang Maass