No abstract

Brain function relies on the coordination of activity across multiple, recurrently connected brain areas. For instance, sensory information encoded in early sensory areas is relayed to, and further processed by, higher cortical areas and then fed back. However, the way in which feedforward and feedback signaling interact with one another is incompletely understood. Here we investigate this question by leveraging simultaneous neuronal population recordings in early and midlevel visual areas (V1–V2 and V1–V4). Using a dimensionality reduction approach, we find that population interactions are feedforward-dominated shortly after stimulus onset and feedback-dominated during spontaneous activity. The population activity patterns most correlated across areas were distinct during feedforward- and feedback-dominated periods. These results suggest that feedforward and feedback signaling rely on separate “channels”, which allows feedback signals to not directly affect activity that is fed forward.

Our understanding of the neural basis of perceptual decision making has been built in part on relating co-fluctuations of single neuron responses to perceptual decisions on a trial-by-trial basis. The strength of this relationship is often compared across neurons or brain areas, recorded in different sessions, animals, or variants of a task. We sought to extend our understanding of perceptual decision making in three ways. First, we measured neuronal activity simultaneously in early [primary visual cortex (V1)] and midlevel (V4) visual cortex while macaque monkeys performed a fine orientation discrimination perceptual task. This allowed a direct comparison of choice signals in these two areas, including their dynamics. Second, we asked how our ability to predict animals' decisions would be improved by considering small simultaneously-recorded neuronal populations rather than individual units. Finally, we asked whether predictions would be improved by taking into account the animals' choice and reward histories, which can strongly influence decision making. We found that responses of individual V4 neurons were weakly predictive of decisions, but only in a brief epoch between stimulus offset and the indication of choice. In V1, few neurons showed significant decision-related activity. Analysis of neuronal population responses revealed robust choice-related information in V4 and substantially weaker signals in V1. Including choice-and reward-history information improved performance further, particularly when the recorded populations contained little decision-related information. Our work shows the power of using neuronal populations and decision history when relating neuronal responses to the perceptual decisions they are thought to underlie.

Brain function relies on the coordination of activity across multiple, recurrently connected, brain areas. For instance, sensory information encoded in early sensory areas is relayed to, and further processed by, higher cortical areas and then fed back. However, the way in which feedforward and feedback signaling interact with one another is incompletely understood. Here we investigate this question by leveraging simultaneous neuronal population recordings in early and midlevel visual areas (V1-V2 and V1-V4). Using a dimensionality reduction approach, we find that population interactions are feedforward-dominated shortly after stimulus onset and feedback-dominated during spontaneous activity. The population activity patterns most correlated across areas were distinct during feedforward- and feedback-dominated periods. These results suggest that feedforward and feedback signaling rely on separate "channels", such that feedback signaling does not directly affect activity that is fed forward.

Technological advances now allow us to record from large populations of neurons across multiple brain areas. These recordings may illuminate how communication between areas contributes to brain function, yet a substantial barrier remains: How do we disentangle the concurrent, bidirectional flow of signals between populations of neurons? We therefore propose here a novel dimensionality reduction framework: Delayed Latents Across Groups (DLAG). DLAG disentangles signals relayed in each direction, identifies how these signals are represented by each population, and characterizes how they evolve within and across trials. We demonstrate that DLAG performs well on synthetic datasets similar in scale to current neurophysiological recordings. Then