Extended Data Fig. 11. Maximizing visual inputs for each model LC unit.

To better understand the differences in stimulus preference across the model LC units, we optimized the visual input history that maximized each model LC unit’s response while minimizing responses of all other model LC units (i.e., a ‘one-hot’ maximizing stimulus). a. We considered a large number of candidate stimulus sequences taken from the training dataset of control sessions (500,000 stimulus sequences in total). We fed each stimulus sequence as input into the 1-to-1 network, extracting the responses of the model LC units. We chose the stimulus sequence that maximized a chosen model LC unit’s response while minimizing the responses for all other model LC units. We used the following objective function f_i(x) for the ith chosen model LC unit, adopted from⁶⁴: $f_i\left(\mathbf{x}\right)=\frac{\exp \left(r_i\left(\mathbf{x}\right)\right)}{{\sum }_{j\ne i}\exp \left(r_j\left(\mathbf{x}\right)\right)}$ where x is the visual input sequence of 10 frames and r_i is the response of the ith model LC unit. The objective function f_i(x) is maximized for large responses of the ith model LC unit and responses as small as possible for all other units. Thus, we optimize stimulus sequences as “one-hot maximizations”. b. Maximizing stimulus sequences for each model LC unit with the most recent frame as the top image. One hot maximization worked for a handful of model LC units (LC9, LC10a, LC11, LC12, LC15; top panel shows responses of all model LC units to that stimulus sequence); surprisingly, one-hot maximization failed to drive a single model LC unit for many of the other LC types (at least one black dot has similar value to color dot), indicating that these model LC units share stimulus preferences with other model LC units. Some stimulus sequences have smooth changes to the fictive female’s parameters, such as LC10a and the increase in female size. However, other maximizing stimulus sequences show large jumps of the fictive female (e.g., LC4, LC11, LC12, LC22, etc.); even though these stimulus sequences were chosen from natural courtship, they likely represent outliers that strongly drive responses. This is especially true of model LC11 that prefers a small female moving at a fast speed, consistent with LC11 being a small object detector^27,28. These maximizing stimulus sequences represent predictions of the 1-to-1 network that can be tested in future experiments to see if they truly elicit large responses from LC neurons, much like recent work has identified images to drive visual cortical neurons of macaque monkey^64–67. Other objective functions, such as maximizing the response variation across time with a longer stimulus sequence, and other constraints, such as restricting how much a fictive female may change between consecutive frames or requiring the fictive female to not remain static, are easily possible with the 1-to-1 network. Our main finding here is that many of the one-hot maximizing stimuli failed to only activate the targeted LC type; this is further evidence that visual features are distributed across the LC population.

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