Showing papers by "Byron M. Yu published in 2021"

Learning is shaped by abrupt changes in neural engagement

Abstract: Internal states such as arousal, attention and motivation modulate brain-wide neural activity, but how these processes interact with learning is not well understood During learning, the brain modifies its neural activity to improve behavior How do internal states affect this process? Using a brain-computer interface learning paradigm in monkeys, we identified large, abrupt fluctuations in neural population activity in motor cortex indicative of arousal-like internal state changes, which we term 'neural engagement' In a brain-computer interface, the causal relationship between neural activity and behavior is known, allowing us to understand how neural engagement impacted behavioral performance for different task goals We observed stereotyped changes in neural engagement that occurred regardless of how they impacted performance This allowed us to predict how quickly different task goals were learned These results suggest that changes in internal states, even those seemingly unrelated to goal-seeking behavior, can systematically influence how behavior improves with learning

Feedforward and feedback interactions between visual cortical areas use different population activity patterns

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”, such that feedback signaling does not directly affect activity that is fed forward.

In vivo augmentation of a complex gut bacterial community

Abstract: Efforts to model the human gut microbiome in mice have led to important insights into the mechanisms of host-microbe interactions. However, the model communities studied to date have been defined or complex but not both, limiting their utility. In accompanying work, we constructed a complex synthetic community (104 strains, hCom1) containing the most common taxa in the human gut microbiome. Here, we used an iterative experimental process to improve hCom1 by filling open metabolic and/or anatomical niches. When we colonized germ-free mice with hCom1 and then challenged it with a human fecal sample, the consortium exhibited surprising stability; 89% of the cells and 58% of the taxa derive from the original community, and the pre- and post-challenge communities share a similar overall structure. We used these data to construct a second version of the community, adding 22 strains that engrafted following fecal challenge and omitting 7 that dropped out (119 strains, hCom2). In gnotobiotic mice, hCom2 exhibited increased stability to fecal challenge and robust colonization resistance against pathogenic Escherichia coli. Mice colonized by hCom2 versus human feces are similar in terms of microbiota-derived metabolites, immune cell profile, and bacterial density in the gut, suggesting that this consortium is a prototype of a model system for the human gut microbiome.

A stable population code for attention in prefrontal cortex leads a dynamic attention code in visual cortex.

Abstract: Attention often requires maintaining a stable mental state over time while simultaneously improving perceptual sensitivity. These requirements place conflicting demands on neural populations, as sensitivity implies a robust response to perturbation by incoming stimuli, which is antithetical to stability. Functional specialization of cortical areas provides one potential mechanism to resolve this conflict. We reasoned that attention signals in executive control areas might be highly stable over time, reflecting maintenance of the cognitive state, thereby freeing up sensory areas to be more sensitive to sensory input (i.e., unstable), which would be reflected by more dynamic attention signals in those areas. To test these predictions, we simultaneously recorded neural populations in prefrontal cortex (PFC) and visual cortical area V4 in rhesus macaque monkeys performing an endogenous spatial selective attention task. Using a decoding approach, we found that the neural code for attention states in PFC was substantially more stable over time compared with the attention code in V4 on a moment-by-moment basis, in line with our guiding thesis. Moreover, attention signals in PFC predicted the future attention state of V4 better than vice versa, consistent with a top-down role for PFC in attention. These results suggest a functional specialization of attention mechanisms across cortical areas with a division of labor. PFC signals the cognitive state and maintains this state stably over time, whereas V4 responds to sensory input in a manner dynamically modulated by that cognitive state. SIGNIFICANCE STATEMENT Attention requires maintaining a stable mental state while simultaneously improving perceptual sensitivity. We hypothesized that these two demands (stability and sensitivity) are distributed between prefrontal and visual cortical areas, respectively. Specifically, we predicted attention signals in visual cortex would be less stable than in prefrontal cortex, and furthermore prefrontal cortical signals would predict attention signals in visual cortex in line with the hypothesized role of prefrontal cortex in top-down executive control. Our results are consistent with suggestions deriving from previous work using separate recordings in the two brain areas in different animals performing different tasks and represent the first direct evidence in support of this hypothesis with simultaneous multiarea recordings within individual animals.

Disentangling the flow of signals between populations of neurons

Abstract: Technological advances have granted the ability 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 two populations of neurons? We therefore propose here a novel dimensionality reduction framework: Delayed Latents Across Groups (DLAG). DLAG disentangles signals relayed in both directions; identifies how these signals are represented by each population; and characterizes how they evolve over time and trial-to-trial. We demonstrate that DLAG performs well on synthetic datasets similar in scale to current neurophysiological recordings. Then we study simultaneously recorded populations in primate visual areas V1 and V2, where DLAG reveals signatures of concurrent yet selective communication. Our framework lays the foundation for dissecting the intricate flow of signals across populations of neurons, and how this signaling contributes to cortical computation.