Sampling-Based Representation of Uncertainty: Time is of Essence

Session 4: Critical gaps in the models

What are the new challenges for theoretical models arising from experimental data?

Documents and media

Download support

pdf (6.9 MB)

Speaker(s)

Mate Lengyel

Univ. Cambridge, UK

Events

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

Christopher Summerfield

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

Not recorded

Sampling-Based Representation of Uncertainty: Time is of Essence

Session 4: Critical gaps in the models
Chair: Stanislas Dehaene

Documents and media

Speaker(s)

Mate Lengyel

Events

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

See also

Sampling-Based Representation of Uncertainty: Time is of Essence

Session 4: Critical gaps in the modelsChair: Stanislas Dehaene

Documents and media

Speaker(s)

Mate Lengyel

Events

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

See also

Session 4: Critical gaps in the models
Chair: Stanislas Dehaene