Data from: Dynamic population codes of multiplexed stimulus features in primate area MT

The middle-temporal area (MT) of primate visual cortex is critical in the analysis of visual motion. Single-unit studies suggest that the response dynamics of neurons within area MT depend on stimulus features, but how these dynamics emerge at the population level, and how feature representations interact, is not clear. Here, we used multivariate classification analysis to study how stimulus features are represented in the spiking activity of populations of neurons in area MT of marmoset monkey. Using Representational Similarity Analysis (RSA) we distinguished the emerging representations of moving grating and dot field stimuli. We show that representations of stimulus orientation, spatial frequency and speed are evident near the onset of the population response, while the representation of stimulus direction is slower to emerge and sustained throughout the stimulus-evoked response. We further found a spatiotemporal asymmetry in the emergence of direction representations. Representations for high spatial frequencies and low temporal frequencies are initially orientation-dependent, while those for high temporal frequencies and low spatial frequencies are more sensitive to motion direction. Our analyses reveal a complex interplay of feature representations in area MT population response that may explain the stimulus-dependent dynamics of motion vision.

Usage Notes

Multivariate classifier performance: moving dot field stimuliMultivariate classifier performance (proportion correct) when discriminating moving dot field stimuli (varying in direction and speed) based on multi electrode recordings from marmoset area MT. All data is stored in Matlab data (.mat) files that were created with Matlab 2013a.Moving_dot_field_classification_performance.matSpike counts: moving dot field stimuliSpike counts for each electrode from marmoset area MT recorded while the anaesthetised animal was shown moving dot field stimuli. All data is stored in Matlab data (.mat) files that were created with Matlab 2013a.Moving_dot_field_spike_counts.zipMultivariate classifier performance: moving grating stimuliMultivariate classifier performance (proportion correct) when discriminating moving grating stimuli (varying in direction and speed) based on multi electrode recordings from marmoset area MT. All data is stored in Matlab data (.mat) files that were created with Matlab 2013a.Moving_grating_classification_performance.matSpike counts: moving grating stimuliSpike counts for each electrode from marmoset area MT recorded while the anaesthetised animal was shown moving dot field stimuli. All data is stored in Matlab data (.mat) files that were created with Matlab 2013aMoving_grating_spike_counts.zip