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Inter-area communication: theory
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Classroom Contents
Cortical Areas Interact through a Communication Subspace
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- 1 Intro
- 2 Linear readout from a population
- 3 (1) demix dependencies on task parameters
- 4 Algorithm Step 1: decompose PSTHS into marginalized averages
- 5 Condition-independent components: Only part of manifold is experimentally controlled
- 6 Inter-area communication: theory
- 7 Inter-area communication: experiment
- 8 Simultaneous recordings in V1 output layers and V2 input layers of macaque
- 9 Population activity quantified as spike counts
- 10 Relating V1 population activity to response of a single V2 neuron
- 11 Predicting the V2 population Could predictive dimensions span a subspace?
- 12 Control: Is V2 activity lower dimensional than V1 activity?
- 13 V1-V2 interaction is low dimensional
- 14 Controls
- 15 Do predictive dimensions match dominant dimensions?
- 16 Predictive dimensions do not match dominant dimensions
- 17 prediction of target V1 activity relies on source V1 dominant modes
