Abstract The visual cortex is organized hierarchically, but the presence of extensive recurrent and parallel pathways make it challenging to decipher how signals flow between neuronal populations. Here, we tracked the flow of spiking activity recorded from six interconnected levels of the mouse visual hierarchy. By analyzing leading and lagging spike-timing relationships among pairs of simultaneously recorded neurons, we created a cellular-scale directed network graph. Using a module-detection algorithm to cluster neurons based on shared connectivity patterns, we uncovered two multi-regional communication modules distributed across the hierarchy. The direction of signal flow between and within these modules, differences in layer and area distributions, and distinct temporal dynamics suggest that one module is positioned to transmit feedforward sensory signals, whereas the other integrates inputs for recurrent processing. These results suggest that multi-regional functional modules may be a fundamental feature of organization beyond cortical areas that supports signal propagation across hierarchical recurrent networks.
Recent advances in neuroscientific experimental techniques have enabled us to simultaneously record the activity of thousands of neurons across multiple brain regions. This has led to a growing need for computational tools capable of analyzing how task-relevant information is represented and communicated between several brain regions. Partial information decompositions (PIDs) have emerged as one such tool, quantifying how much unique, redundant and synergistic information two or more brain regions carry about a task-relevant message. However, computing PIDs is computationally challenging in practice, and statistical issues such as the bias and variance of estimates remain largely unexplored. In this paper, we propose a new method for efficiently computing and estimating a PID definition on multivariate Gaussian distributions. We show empirically that our method satisfies an intuitive additivity property, and recovers the ground truth in a battery of canonical examples, even at high dimensionality. We also propose and evaluate, for the first time, a method to correct the bias in PID estimates at finite sample sizes. Finally, we demonstrate that our Gaussian PID effectively characterizes inter-areal interactions in the mouse brain, revealing higher redundancy between visual areas when a stimulus is behaviorally relevant.
Abstract The mammalian visual system, from retina to neocortex, has been extensively studied at both anatomical and functional levels. Anatomy indicates the cortico-thalamic system is hierarchical, but characterization of cellular-level functional interactions across multiple levels of this hierarchy is lacking, partially due to the challenge of simultaneously recording activity across numerous regions. Here, we describe a large, open dataset (part of the Allen Brain Observatory ) that surveys spiking from units in six cortical and two thalamic regions responding to a battery of visual stimuli. Using spike cross-correlation analysis, we find that inter-area functional connectivity mirrors the anatomical hierarchy from the Allen Mouse Brain Connectivity Atlas . Classical functional measures of hierarchy, including visual response latency, receptive field size, phase-locking to a drifting grating stimulus, and autocorrelation timescale are all correlated with the anatomical hierarchy. Moreover, recordings during a visual task support the behavioral relevance of hierarchical processing. Overall, this dataset and the hierarchy we describe provide a foundation for understanding coding and dynamics in the mouse cortico-thalamic visual system.
Abstract Extracellular electrophysiology and two-photon calcium imaging are widely used methods for measuring physiological activity with single-cell resolution across large populations of neurons in the brain. While these two modalities have distinct advantages and disadvantages, neither provides complete, unbiased information about the underlying neural population. Here, we compare evoked responses in visual cortex recorded in awake mice under highly standardized conditions using either imaging or electrophysiology. Across all stimulus conditions tested, we observe a larger fraction of responsive neurons in electrophysiology and higher stimulus selectivity in calcium imaging. This work explores which data transformations are most useful for explaining these modality-specific discrepancies. We show that the higher selectivity in imaging can be partially reconciled by applying a spikes-to-calcium forward model to the electrophysiology data. However, the forward model could not reconcile differences in responsiveness without sub-selecting neurons based on event rate or level of signal contamination. This suggests that differences in responsiveness more likely reflect neuronal sampling bias or cluster-merging artifacts during spike sorting of electrophysiological recordings, rather than flaws in event detection from fluorescence time series. This work establishes the dominant impacts of the two modalities’ respective biases on a set of functional metrics that are fundamental for characterizing sensory-evoked responses.
Abstract Cells of different types perform diverse computations and coordinate their activity during sensation, perception, and action. While electrophysiological approaches can measure the activity of many neurons simultaneously, assigning cell type labels to these neurons is an open problem. Here, we develop PhysMAP, a framework that weighs multiple electrophysiological modalities simultaneously in an unsupervised manner and obtain an interpretable representation that separates neurons by cell type. PhysMAP is superior to any single electrophysiological modality in identifying neuronal cell types such as excitatory pyramidal, PV + interneurons, and SOM + interneurons with high confidence in both juxtacellular and extracellular recordings and from multiple areas of the mouse brain. PhysMAP built on ground truth data can be used for classifying cell types in new and existing electrophysiological datasets, and thus facilitate simultaneous assessment of the coordinated dynamics of multiple neuronal cell types during behavior.
Neuropixels recordings from mouse visual cortex. The dataset was used in the paper: Multi-regional module-based signal transmission in mouse visual cortex, Jia et al. (2022) Neuron. For information about experimental procedures, see Siegle, Jia et al. (2021) Nature 592, 86-92 (https://www.nature.com/articles/s41586-020-03171-x). For information about file contents, see https://allensdk.readthedocs.io/en/latest/visual_coding_neuropixels.html The NWB 1.0 files can be opened with HDF5 and HDFview. Mouse IDGenotype 306046 ['Sst-IRES-Cre/wt;Ai32/wt'] 388523 ['Pvalb-Cre',] 389262 ['Vip-Cre'] 408153 ['Sst-IRES-Cre/wt;Ai32/wt'] 410344 ['Vip-Cre'] 415149 ['wt/wt'] 412809 ['wt/wt'] 412804 ['wt/wt'] 416856 ['Sst-IRES-Cre/wt;Ai32/wt'] 419114 ['wt/wt'] 419117 ['wt/wt'] 419118 ['wt/wt'] 419119 ['wt/wt'] 424445 ['wt/wt'] 415148 ['wt/wt'] 416356 ['Sst-IRES-Cre/wt;Ai32/wt'] 416861 ['Sst-IRES-Cre/wt;Ai32/wt'] 419112 ['wt/wt'] 419116 ['wt/wt']
Abstract The visual cortex is organized hierarchically, but extensive recurrent pathways make it challenging to decipher the flow of information with single neuron resolution. Here, we characterize spiking interactions in populations of neurons from six interconnected areas along the visual hierarchy in awake mice. We generated multi-area, directed graphs of neuronal communication and uncovered two spatially-distributed functional modules. One module is positioned to transmit feedforward sensory signals along the hierarchy, while the other receives convergent input and engages in recurrent processing. The modules differ in layer and area distributions, convergence and divergence, and population-level temporal dynamics. These results reveal a neuronal-resolution cortical network topology in which distinct processing modules are interlaced across multiple areas of the cortical hierarchy.
