Abstract
During fixation and between saccades, our eyes undergo diffusive random motion called fixational drift. The role of fixational drift in visual coding and inference has been debated in the past few decades, but the mechanisms that underlie this motion remained unknown. In particular, it has been unclear whether fixational drift arises from peripheral sources, or from central sources within the brain. Here we show that fixational drift is correlated with neural activity, and identify its origin in central neural circuitry within the oculomotor system, upstream to the ocular motoneurons (OMNs). We analyzed a large data set of OMN recordings in the rhesus monkey, alongside precise measurements of eye position, and found that most of the variance of fixational eye drifts must arise upstream of the OMNs. The diffusive statistics of the motion points to the oculomotor integrator, a memory circuit responsible for holding the eyes still between saccades, as a likely source of the motion. Theoretical modeling, constrained by the parameters of the primate oculomotor system, supports this hypothesis by accounting for the amplitude as well as the statistics of the motion. Thus, we propose that fixational ocular drift provides a direct observation of diffusive dynamics in a neural circuit responsible for storage of continuous parameter memory in persistent neural activity. The identification of a mechanistic origin for fixational drift is likely to advance the understanding of its role in visual processing and inference.
Original language | American English |
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Article number | 1697 |
Journal | Nature Communications |
Volume | 13 |
Issue number | 1 |
DOIs | |
State | Published - Dec 2022 |
Bibliographical note
Funding Information:The authors would like to thank Gilad Ben-Shushan for providing Fig. 4 a. This research was supported by the Israel Science Foundation grant No. 1733/13 and grant No. 1745/18, and in part by the Israel Science Foundation grant No. 1978/13. We acknowledge additional support from the Swartz foundation and from the Center for Brains, Minds and Machines (N.S.), the Israel Science Foundation grant No. 380/17 and the European Research Council grant No. 755745 (M.J.), and the Gatsby Charitable Foundation (Y.B.). This work was done in part while visiting the Simons Institute for the Theory of Computing (N.B.-S., N.S., and Y.B.). Y.B. is the incumbent of the William N. Skirball Chair in Neurophysics.
Funding Information:
The authors would like to thank Gilad Ben-Shushan for providing Fig. a. This research was supported by the Israel Science Foundation grant No. 1733/13 and grant No. 1745/18, and in part by the Israel Science Foundation grant No. 1978/13. We acknowledge additional support from the Swartz foundation and from the Center for Brains, Minds and Machines (N.S.), the Israel Science Foundation grant No. 380/17 and the European Research Council grant No. 755745 (M.J.), and the Gatsby Charitable Foundation (Y.B.). This work was done in part while visiting the Simons Institute for the Theory of Computing (N.B.-S., N.S., and Y.B.). Y.B. is the incumbent of the William N. Skirball Chair in Neurophysics.
Publisher Copyright:
© 2022, The Author(s).