Attention maps reveal stimulus-dependent retinal population codes
María-José Escobar
IntroductionUnderstanding how deep learning models map neural population activity to stimuli requires both high predictive accuracy and interpretable internal mechanisms.MethodsIn this work, we employ the POYO framework, a scalable transformer architecture based on spike tokenization and latent modeling, to decode large-scale retinal ganglion cell recordings. We ask whether the model's attention mechanisms can provide biologically meaningful insight by evaluating two contrasting conditions: a un