Learning how to experience the world: From circuits to cell types to genes

Area of Science:

• Neuroscience
• Molecular Biology
• Computational Neuroscience

Background:

• Sensory perception relies on transforming complex sensory data into simplified internal brain models.
• Two key frameworks, representational processing and predictive processing, explain how the brain achieves perception.
• The neural circuit mechanisms and potential unification of these frameworks remain unclear.

Purpose of the Study:

• To review recent studies on mouse primary sensory and higher-order association cortices.
• To demonstrate how cell-type-specific transcriptional programs mechanistically link representational and predictive processing frameworks.
• To explore how gene expression in specific cell types supports circuit functions and adaptive behaviors.

Main Methods:

• Review of recent studies in mouse primary sensory and higher-order association cortex.
• Analysis of cell-type-specific transcriptional programs and their relation to neural circuit functions.
• Examination of gene expression patterns in distinct neuronal populations.

Main Results:

• In primary sensory cortices, distinct molecularly defined cell types act as feature detectors or signal sensory prediction errors.
• Higher-order association cortices show that inhibitory cell-type composition and plasticity genes support both representational and predictive learning.
• Transcriptional programs enable cell types and circuits to support both representational and predictive processing in a behavior-dependent manner.

Conclusions:

• Cell-type-specific gene expression provides a mechanistic link between representational and predictive processing frameworks.
• These molecular programs equip specific cell types with computational properties crucial for sensory perception.
• Understanding gene expression in cell types offers a unified approach to deciphering neural implementations of sensory perception and adaptive behavior.