Latent encoding of movement in primary visual cortex

Abstract

Neurons in the primary visual cortex (V1) are classically thought to encode spatial features of visual stimuli through simple population codes: each neuron exhibits a preferred orientation and preferred spatial frequency, both of which remaining invariant to other aspects of the visual stimulus. Here, we show that this simple rule does not apply to the representation of major features of stimulus motion, including stimulus direction and temporal frequency (TF). We collected an extensive dataset of cat (of either sex) V1 responses to stimuli covarying in orientation, direction, spatial frequency, and TF to assess the extent of motion selectivity. We show that preferred TF is mostly uniform across the cortical surface. Yet, in over half of V1, the preferred direction is reversed with changing stimulus TF, revealing four distinct map motifs embedded in V1s functional architecture. Similarly, despite the lack of spatial modulation for the preferred TF map and the lack of invariance for the preferred direction map, we found using convolutional neural networks that direction, TF and stimulus speed can be accurately decoded from V1 responses at all cortical locations. These findings suggest that subtle modulations of V1 activity may convey fine information about stimulus motion, pointing to a novel primary sensory encoding mechanism despite complex co-variation of responses to multiple attributes across V1 neurons. Significance StatementUnderstanding how the external world is represented in the brain has long been a central endeavor in neuroscience. In the mammalian primary visual cortex (V1), groups of neurons encode stimulus properties through changes in activity, with "preferred" stimuli eliciting maximal responses, as demonstrated for orientation and spatial frequency. Using an extensive dataset of neuronal responses, we investigated whether V1 also encodes motion speed. We found that cortical response organization remains largely invariant, with neurons exhibiting a uniform preference for temporal frequency. Yet, machine learning algorithms decode motion speed with remarkable precision, revealing that subtle, spatially distributed modulations of activity underlie speed encoding. Moreover, we show that preferred direction flips with speed across 80% of cortex, uncovering a novel co-organization of motion and direction information in V1.