Electrophysiology has long been the workhorse of neuroscience, allowing scientists to record with millisecond precision the action potentials generated by neurons in vivo. Recently, calcium imaging of fluorescent indicators has emerged as a powerful alternative. This technique has its own strengths and weaknesses and unique data processing problems and interpretation confounds. Here we review the computational methods that convert raw calcium movies to estimates of single neuron spike times with minimal human supervision. By computationally addressing the weaknesses of calcium imaging, these methods hold the promise of significantly improving data quality. We also introduce a new metric to evaluate the output of these processing pipelines, which is based on the cluster isolation distance routinely used in electrophysiology.

Limited color channels in fluorescence microscopy have long constrained spatial analysis in biological specimens. Here, we introduce cycle Hybridization Chain Reaction (HCR), a method that integrates multicycle DNA barcoding with HCR to overcome this limitation. cycleHCR enables highly multiplexed imaging of RNA and proteins using a unified barcode system. Whole-embryo transcriptomics imaging achieved precise three-dimensional gene expression and cell fate mapping across a specimen depth of ~310 μm. When combined with expansion microscopy, cycleHCR revealed an intricate network of 10 subcellular structures in mouse embryonic fibroblasts. In mouse hippocampal slices, multiplex RNA and protein imaging uncovered complex gene expression gradients and cell-type-specific nuclear structural variations. cycleHCR provides a quantitative framework for elucidating spatial regulation in deep tissue contexts for research and potentially diagnostic applications.

bioRxiv preprint: 10.1101/2024.05.17.594641

The brain exhibits rich oscillatory dynamics that vary across tasks and states, such as the EEG oscillations that define sleep. These oscillations play critical roles in cognition and arousal, but the brainwide mechanisms underlying them are not yet described. Using simultaneous EEG and fast fMRI in subjects drifting between sleep and wakefulness, we developed a machine learning approach to investigate which brainwide fMRI dynamics predict alpha (8-12 Hz) and delta (1-4 Hz) rhythms. We predicted moment-by-moment EEG power from fMRI activity in held-out subjects, and found that information about alpha power was represented by a remarkably small set of regions, segregated in two distinct networks linked to arousal and visual systems. Conversely, delta rhythms were diffusely represented on a large spatial scale across the cortex. These results identify distributed networks that predict delta and alpha rhythms, and establish a computational framework for investigating fMRI brainwide dynamics underlying EEG oscillations.

Recent studies in mice have shown that orofacial behaviors drive a large fraction of neural activity across the brain. To understand the nature and function of these signals, we need better computational models to characterize the behaviors and relate them to neural activity. Here we developed Facemap, a framework consisting of a keypoint tracking algorithm and a deep neural network encoder for predicting neural activity. We used the Facemap keypoints as input for the deep neural network to predict the activity of ∼50,000 simultaneously-recorded neurons and in visual cortex we doubled the amount of explained variance compared to previous methods. Our keypoint tracking algorithm was more accurate than existing pose estimation tools, while the inference speed was several times faster, making it a powerful tool for closed-loop behavioral experiments. The Facemap tracker was easy to adapt to data from new labs, requiring as few as 10 annotated frames for near-optimal performance. We used Facemap to find that the neuronal activity clusters which were highly driven by behaviors were more spatially spread-out across cortex. We also found that the deep keypoint features inferred by the model had time-asymmetrical state dynamics that were not apparent in the raw keypoint data. In summary, Facemap provides a stepping stone towards understanding the function of the brainwide neural signals and their relation to behavior.

Single neurons in visual cortex provide unreliable measurements of visual features due to their high trial-to-trial variability. It is not known if this “noise” extends its effects over large neural populations to impair the global encoding of sensory stimuli. We recorded simultaneously from ∼20,000 neurons in mouse visual cortex and found that the neural population had discrimination thresholds of 0.3° in an orientation decoding task. These thresholds are ∼100 times smaller than those reported behaviorally in mice. The discrepancy between neural and behavioral discrimination could not be explained by the types of stimuli we used, by behavioral states or by the sequential nature of trial-by-trial perceptual learning tasks. These results imply that the limits of sensory perception in mice are not set by neural noise in sensory cortex, but by the limitations of downstream decoders.

A neuronal population encodes information most efficiently when its activity is uncorrelated and high-dimensional, and most robustly when its activity is correlated and lower-dimensional. Here, we analyzed the correlation structure of natural image coding, in large visual cortical populations recorded from awake mice. Evoked population activity was high dimensional, with correlations obeying an unexpected power-law: the n-th principal component variance scaled as 1/n. This was not inherited from the 1/f spectrum of natural images, because it persisted after stimulus whitening. We proved mathematically that the variance spectrum must decay at least this fast if a population code is smooth, i.e. if small changes in input cannot dominate population activity. The theory also predicts larger power-law exponents for lower-dimensional stimulus ensembles, which we validated experimentally. These results suggest that coding smoothness represents a fundamental constraint governing correlations in neural population codes.

Individual neurons in visual cortex provide the brain with unreliable estimates of visual features. It is not known whether the single-neuron variability is correlated across large neural populations, thus impairing the global encoding of stimuli. We recorded simultaneously from up to 50,000 neurons in mouse primary visual cortex (V1) and in higher order visual areas and measured stimulus discrimination thresholds of 0.35° and 0.37°, respectively, in an orientation decoding task. These neural thresholds were almost 100 times smaller than the behavioral discrimination thresholds reported in mice. This discrepancy could not be explained by stimulus properties or arousal states. Furthermore, behavioral variability during a sensory discrimination task could not be explained by neural variability in V1. Instead, behavior-related neural activity arose dynamically across a network of non-sensory brain areas. These results imply that perceptual discrimination in mice is limited by downstream decoders, not by neural noise in sensory representations.

Cortical networks exhibit intrinsic dynamics that drive coordinated, large-scale fluctuations across neuronal populations and create noise correlations that impact sensory coding. To investigate the network-level mechanisms that underlie these dynamics, we developed novel computational techniques to fit a deterministic spiking network model directly to multi-neuron recordings from different rodent species, sensory modalities, and behavioral states. The model generated correlated variability without external noise and accurately reproduced the diverse activity patterns in our recordings. Analysis of the model parameters suggested that differences in noise correlations across recordings were due primarily to differences in the strength of feedback inhibition. Further analysis of our recordings confirmed that putative inhibitory neurons were indeed more active during desynchronized cortical states with weak noise correlations. Our results demonstrate that network models with intrinsically-generated variability can accurately reproduce the activity patterns observed in multi-neuron recordings and suggest that inhibition modulates the interactions between intrinsic dynamics and sensory inputs to control the strength of noise correlations.

Cognitive maps confer animals with flexible intelligence by representing spatial, temporal and abstract relationships that can be used to shape thought, planning and behaviour. Cognitive maps have been observed in the hippocampus1, but their algorithmic form and learning mechanisms remain obscure. Here we used large-scale, longitudinal two-photon calcium imaging to record activity from thousands of neurons in the CA1 region of the hippocampus while mice learned to efficiently collect rewards from two subtly different linear tracks in virtual reality. Throughout learning, both animal behaviour and hippocampal neural activity progressed through multiple stages, gradually revealing improved task representation that mirrored improved behavioural efficiency. The learning process involved progressive decorrelations in initially similar hippocampal neural activity within and across tracks, ultimately resulting in orthogonalized representations resembling a state machine capturing the inherent structure of the task. This decorrelation process was driven by individual neurons acquiring task-state-specific responses (that is, 'state cells'). Although various standard artificial neural networks did not naturally capture these dynamics, the clone-structured causal graph, a hidden Markov model variant, uniquely reproduced both the final orthogonalized states and the learning trajectory seen in animals. The observed cellular and population dynamics constrain the mechanisms underlying cognitive map formation in the hippocampus, pointing to hidden state inference as a fundamental computational principle, with implications for both biological and artificial intelligence.

All brain functions in animals rely upon neuronal connectivity that is established during early development. Although the activity-dependent mechanisms are deemed important for brain development and adult synaptic plasticity, the precise cellular and molecular mechanisms remain however, largely unknown. This lack of fundamental knowledge regarding developmental neuronal assembly owes its existence to the complexity of the mammalian brain as cell-cell interactions between individual neurons cannot be investigated directly. Here, we used individually identified synaptic partners from Lymnaea stagnalis to interrogate the role of neuronal activity patterns over an extended time period during various growth time points and synaptogenesis. Using intracellular recordings, microelectrode arrays, and time-lapse imaging, we identified unique patterns of activity throughout neurite outgrowth and synapse formation. Perturbation of voltage-gated Ca channels compromised neuronal growth patterns which also invoked a protein kinase A mediated pathway. Our findings underscore the importance of unique activity patterns in regulating neuronal growth, neurite branching, and synapse formation, and identify the underlying cellular and molecular mechanisms.