Possible Optimal Strategies for Orientation Coding in Macaque V1 Revealed with a Self-Attention Deep Neural Network (SA-DNN) Model

The orientation tuning bandwidths of individual V1 neurons are not sufficiently narrow to support fine psychophysical orientation discrimination thresholds. Here we explore the possibility that V1 neurons as a population may apply optimal orientation coding strategies to achieve superb orientation tuning. We trained a self-attention deep neural network (SA-DNN) model to reconstruct a Gabor stimulus image from neuronal responses obtained through two-photon calcium imaging in five awake macaques. Each response field of view (FOV) contains 1400-1700 neurons, and their responses to a Gabor stimulus are used as the model inputs. The SA-DNN model consists of a self-attention mechanism followed by a feedforward layer. The self-attention mechanism can reveal cooperative coding by neurons activated by the Gabor stimulus, yielding attention maps that display two-way connections among neurons. The results suggest: (1) Neurons tuned to the stimulus orientation tend to have higher attention scores with all other neurons. The top 25% of orientation-tuned neurons with the highest mean attention scores can best reconstruct the stimulus images, while the bottom 50% neurons are unable to do so. (2) The responses of the top 25% neurons, after self-attention transformation, generate significantly sharpened population orientation tuning functions, with the amplitude increased by 3-5 times and bandwidth narrowed by approximately 30%. (3) After excluding the self-attention component, the forward propagation of the model would only reconstruct very coarse stimulus images. (4) The tuning sharpening displays an oblique effect: attention maps have higher variabilities at cardinal than at oblique orientations, producing more sharpened orientation tuning functions at cardinal orientations. These modeling results suggest that the self-attention mechanisms optimize orientation coding in macaque V1, reweighting responses to accentuate neurons based on attention scores. The results provide new insights into V1 neuronal connectivity, elaborating how self-attention refines neuronal interactions and reweights responses to process orientation information.