Biology subjects

Runyan, C. A.

Publications and source records attributed to Runyan, C. A..

5 recordsLinked to original sources

bioRxiv2024

Different state-dependence of population codes across cortex

During perceptual decision-making tasks, behavioral performance and its neural correlates vary with changes in internal states such as arousal, motivation, and strategy. It is not yet clear whether these state fluctuations differently impact the distributed neural processes that underlie task performance, from sensory processing to control of motor outputs. The posterior parietal cortex (PPC) is an association-level cortical region implicated in sensorimotor transformations, but evidence of its causal requirement in decision-making tasks has been contradictory. We trained mice to perform a navigation-based sound localization task and asked how natural fluctuations in behavioral state related to neural processing in association areas during decision-making. Behavioral performance in expert-level mice was not static but instead transitioned between periods of near-perfect performance and biased, less accurate performance. Using a hidden Markov model, we could reliably define these as distinct strategies that included a high-performance state where mice used relevant stimulus information to inform choices, and two biased states where mice weighted stimulus information less strongly. Optogenetic inactivation of PPC decreased task performance accuracy, and the model captured the resulting change in behavioral strategy as a reduction in the weighting of the auditory cues and an increase in behavioral bias, in predicting mices decisions. Two-photon calcium imaging revealed that performance states strongly influenced population activity patterns in PPC, but not primary auditory cortex (AC). Surprisingly, activity of individual PPC neurons was better explained by external inputs and behavioral variables during biased behavioral performance, while shared variability across neurons in PPC was strongest in the high-performance state. Together, these findings suggest that neural activity in parietal cortex is causally required for decisions and is linked to behavioral performance states.

neuroscience↗

bioRxiv2024

Wider spread of excitatory neuron influence in association cortex than sensory cortex

The basic structure of local cortical circuits, including the composition of cell types, is highly conserved across the cortical processing hierarchy. However, computational roles and the spatial and temporal properties of population activity differ fundamentally in sensory-level and association-level areas. In primary sensory cortex, the timescale of population activity is shorter and pairwise correlations decay more rapidly over distance between neurons, supporting a population code that is suited to encoding rapidly fluctuating sensory stimuli. In association cortex, the timescale of population activity is longer, and pairwise correlations are stronger over wider distances, a code that is suited to holding information in memory and driving behavior. Here, we tested whether these differences in population codes could potentially be explained by intrinsic differences in local network structure. We targeted single excitatory neurons optogenetically, while monitoring the surrounding ongoing population activity in sensory (auditory cortex) and association (posterior parietal cortex) areas in mice. While the temporal impacts of these perturbations were similar across regions, the spatial spread of excitatory influence was wider in association cortex than in sensory cortex. Our findings suggest that differences in recurrent connectivity could contribute to the different properties of population codes in sensory and association cortex, and imply that circuit models of cortical function should be tailored to the properties specific to individual regions. Significance statementCell-type-specific functional interactions and connectivity patterns have largely been studied in sensory cortex. Yet the properties of local network activity differ dramatically across the cortical hierarchy, possibly due to differences in intrinsic connectivity patterns. Here, we compared the functional impacts of individual excitatory neurons on local population activity, finding differences in the spatial spread of excitatory influence across regions. Our findings suggest that the structure of local networks differs across the cortical processing hierarchy, and these differences should be considered in circuit models of processes such as decision-making and working memory.

neuroscience↗

bioRxiv2023

Population-level interactions among PV, SOM, and Pyramidal neurons in cortex

Cortical interneurons shape network activity in cell type-specific ways, and are also influenced by interactions with other cell types. These specific cell-type interactions are understudied, as transgenic labeling methods typically restrict labeling to one neuron type at a time. Although recent methods have enabled post-hoc identification of cell types, these are not available to many labs. Here, we present a method to distinguish between two red fluorophores in vivo, which allowed imaging of activity in somatostatin (SOM), parvalbumin (PV), and putative pyramidal neurons (PYR) in mouse association cortex. We compared population events of elevated activity and observed that the PYR network state corresponded to the ratio between mean SOM and PV neuron activity, demonstrating the importance of simultaneous labeling to explain dynamics. These results extend previous findings in sensory cortex, as activity became sparser and less correlated when the ratio between SOM and PV activity was high.

neuroscience↗

bioRxiv2022

Specialization in somatostatin subnetworks in sensory and association cortex

Arousal powerfully influences cortical activity, in part by modulating local inhibitory circuits. Somatostatin-expressing inhibitory interneurons (SOM) are particularly well-situated to shape local population activity in response to shifts in arousal, yet the relationship between arousal state and SOM activity has not been characterized outside of sensory cortex. To determine whether SOM activity is similarly modulated by behavioral state across different levels of the cortical processing hierarchy, we compared the behavioral modulation of SOM neurons in auditory cortex (AC), a primary sensory region, and posterior parietal cortex (PPC), an association-level region of cortex. Behavioral state modulated activity differently in AC and PPC. In PPC, transitions to high arousal were accompanied by large increases in activity across the full PPC neural population, especially in SOM neurons. In AC, arousal transitions led to more subtle changes in overall activity, as individual SOM and Non-SOM neurons could be either positively or negatively modulated during transitions to high arousal states. The coding of sensory information in population activity was enhanced during periods of high arousal in AC, but not PPC. Our findings suggest unique relationships between activity in local circuits and arousal across cortex, which may be tailored to the roles of specific cortical regions in sensory processing or the control of behavior. Significance statementThe effects of arousal on brain networks are profound, but vary across regions. Somatostatin neurons may carry out some of the effects of arousal on local network activity in sensory cortex, by modulating response gain and decorrelating population activity. However, SOM neurons have not been well studied outside of sensory cortex, and so it is unknown whether SOM neurons are similarly affected by shifts in brain state throughout the cortex. Here, we have revealed specialization in the relationship between arousal and activity in SOM neurons that could contribute to the diversity of arousal-related impacts on local computation across cortical regions.

neuroscience↗

bioRxiv2020

Correlations enhance the behavioral readout of neural population activity in association cortex

The spatiotemporal structure of activity in populations of neurons is critical for accurate perception and behavior. Experimental and theoretical studies have focused on "noise" correlations - trial-to-trial covariations in neural activity for a given stimulus - as a key feature of population activity structure. Much work has shown that these correlations limit the stimulus information encoded by a population of neurons, leading to the widely-held prediction that correlations are detrimental for perceptual discrimination behaviors. However, this prediction relies on an untested assumption: that the neural mechanisms that read out sensory information to inform behavior depend only on a populations total stimulus information independently of how correlations constrain this information across neurons or time. Here we make the critical advance of simultaneously studying how correlations affect both the encoding and the readout of sensory information. We analyzed calcium imaging data from mouse posterior parietal cortex during two perceptual discrimination tasks. Correlations limited the ability to encode stimulus information, but (seemingly paradoxically) correlations were higher when mice made correct choices than when they made errors. On a single-trial basis, a mouses behavioral choice depended not only on the stimulus information in the activity of the population as a whole, but unexpectedly also on the consistency of information across neurons and time. Because correlations increased information consistency, sensory information was more efficiently converted into a behavioral choice in the presence of correlations. Given this enhanced-by-consistency readout, we estimated that correlations produced a behavioral benefit that compensated or overcame their detrimental information-limiting effects. These results call for a re-evaluation of the role of correlated neural activity, and suggest that correlations in association cortex can benefit task performance even if they decrease sensory information.

neuroscience↗