Speaker
Description
Economic choice involves comparing the subjective values of goods, a process linked to the orbitofrontal cortex (OFC). Neurophysiological studies have identified OFC neurons encoding variables that capture both the inputs and outputs of this process, such as the offer values and the chosen goods. However, the precise nature of these representations and the circuit mechanisms that give rise to choice remain open questions. To investigate these mechanisms, we combined large-scale neuronal recordings, network inference analyses, and theoretical modeling of circuit dynamics. We used simultaneous recordings of 50–150 neurons while monkeys performed a binary juice choice task and inferred Ising (Hopfield) models using the Adaptive Cluster Expansion algorithm. As an initial step, we characterized each neuron's tuning by identifying the variable it most strongly encoded. We then simulated the inferred network models under different offer inputs, and analyzed their stationary points. Simulations revealed attractor-like avalanche dynamics: stimulating offer value cells associated with one particular good (A or B) induced avalanches that led to the co-activation of other cell groups. Co-activated cells predominantly encoded the corresponding choice outcome (A or B). Compared to random networks, inferred OFC networks showed stronger avalanches. Ablation analyses indicated that direct connections between offer-value and chosen-good cells were crucial for generating correct decision-related avalanches, while other connections played a secondary, reinforcing role. These results suggest that OFC functions as an attractor network transforming offer values into binary choices.
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