Projects

Naturalistic 3D vision   |   Persistent activity and memory   

 

 

Naturalistic 3D vision

We have focused on understanding how the dynamic patterns of light falling upon the two retinae are decoded to extract the trajectory of moving objects through 3D. To date, this line of work has produced convergent evidence that motion processing stages typically thought to encode flat (2D) motion signals actually perform inter-ocular computations to represent 3D directions. We are now extending our understanding of how these more realistic motions are processed by moving into the study of naturally-occurring visual motions that are sculpted by eye and body movements. This work entails wireless eyetracking and neurophysiology, as well as analysis of natural image statistics and continuous behaviors.

A review:

Huk, A.C., Czuba, T.B., Knoell, J., & Cormack, L.K. (2017). Binocular mechanisms of 3D motion processingAnnual Review of Vision Science[link] 

Representative empirical publications:

Bonnen, K.T., Czuba, T.B., Whritner, J., Kohn, A., Huk, A.C., & Cormack, L.K.  (2020). Binocular viewing geometry shapes the neural representation of the dynamic three-dimensional environment. Nature Neuroscience, 23: 113–121. [link] [code&data]

Czuba, T.B., Huk, A.C., Cormack, L.K. & Kohn, A. (2014). Area MT encodes three-dimensional motion. Journal of Neuroscience, 34(47):15522–15533. [pdf]

Rokers, B., Cormack, L.K., & Huk, A.C. (2009). Disparity- and velocity- based signals for 3D motion perception in human MT+Nature Neuroscience, 12(8), 1050-1055. [pdf]

 

Persistent activity and memory

Persistent neural activity is classically thought of as the core computation that allows the brain to remember and contemplate information acquired from the senses. It is qualitatively ubiquitous in the primate brain, but exactly what the time course of persistent activity reflects, and how it is generated and maintained, are only starting to become tractable given new biological and analytic techniques. We are using a variety of neurophysiological and computational techniques to understand this phenomenon.

Representative empirical publications:

Hart, E., & Huk, A.C. (2020). Recurrent circuit dynamics underlie persistent activity in the macaque frontoparietal network. eLife, 9:e52460. [link]

Yates, J.L., Park, I.M., Katz, L.N., Pillow, J.P., & Huk, A.C. (2017). Functional dissection of signal and noise in MT and LIP during decision-makingNature Neuroscience, 20, 1285–1292. [link]

A review:

Huk, A.C., Katz, L.N., Yates, J.L. (2017). The role of the lateral intraparietal area in (the study of) decision makingAnnual Review of Neuroscience, 40: 349-372. [link]