Inferring decoding strategy from choice probabilities in the presence of noise correlations

The activity of cortical neurons in sensory areas covaries with perceptual decisions, a relationship often quantified by choice probabilities. While choice probabilities have been measured extensively, their interpretation has remained fraught with difficulty. Here, we derive the mathematical relationship between choice probabilities, read-out weights and noise correlations within the standard neural decision making model. Our solution allows us to prove and generalize earlier observations based on numerical simulations, and to derive novel predictions. Importantly, we show how the read-out weight profile, or decoding strategy, can be inferred from experimentally measurable quantities. Furthermore, we present a test to decide whether the decoding weights of individual neurons are optimal, even without knowing the underlying noise correlations. We confirm the practical feasibility of our approach using simulated data from a realistic population model. Our work thus provides the theoretical foundation for a growing body of experimental results on choice probabilities and correlations

A Modality-Specific Feedforward Component of Choice-Related Activity in MT

The activity of individual sensory neurons can be predictive of an animal\u27s choices. These decision signals arise from network properties dependent on feedforward and feedback inputs; however, the relative contributions of these inputs are poorly understood. We determined the role of feedforward pathways to decision signals in MT by recording neuronal activity while monkeys performed motion and depth tasks. During each session, we reversibly inactivated V2 and V3, which provide feedforward input to MT that conveys more information about depth than motion. We thus monitored the choice-related activity of the same neuron both before and during V2/V3 inactivation. During inactivation, MT neurons became less predictive of decisions for the depth task but not the motion task, indicating that a feedforward pathway that gives rise to tuning preferences also contributes to decision signals. We show that our data are consistent with V2/V3 input conferring structured noise correlations onto the MT population