Targeted V1 comodulation supports task-adaptive sensory decisions

Abstract

Sensory-guided behavior requires reliable encoding of stimulus information in neural responses, and task-specific decoding through selective combination of these responses. The former has been the topic of intensive study, but the latter remains largely a mystery. We propose a framework in which shared stochastic modulation of task- informative neurons serves as a label to facilitate downstream decoding. Theoretical analysis and computational simulations demonstrate that a decoder that exploits such a signal can achieve flexible and accurate readout. Using this theoretical framework, we analyze behavioral and physiological data obtained from monkeys performing a visual orientation discrimination task. The responses of recorded V1 neurons exhibit strongly correlated modulation. This modulation is stronger in those neurons that are most informative for the behavioral task and it is substantially reduced in a control condition where recorded neurons are uninformative. We demonstrate that this modulator label can be used to improve downstream decoding within a small number of training trials, consistent with observed behavior. Finally, we find that the trial-by-trial modulatory signal estimated from V1 populations is also present in the activity of simultaneously recorded MT units, and preferentially so if they are task-informative, supporting the hypothesis that it serves as a label for the selection and decoding of relevant downstream neurons.

Unsupervised learning of features and object boundaries from local prediction

“…12, 56] have some similarities with iterative algorithms to compute eigenvectors. Originally, phase coherence between the neurons encoding the same object was proposed [12,46,56], but a gain increase with object based attention [47] or a known random modulation is also sufficient to select a task relevant set of neurons [20,21]. Regardless of the mechanistic implementation of the marker, connectivity information of the type our model extracts would be extremely helpful to explain the gradual spread of object selection [12,46].…”