Pengcheng Zhou

The Spatiotemporal Organization of the Striatum Encodes Action Space

Summary Activity in striatal direct- and indirect-pathway spiny projection neurons (SPNs) is critical for proper movement. However, little is known about the spatiotemporal organization of this activity. We investigated the spatiotemporal organization of SPN ensemble activity in mice during self-paced, natural movements using microendoscopic imaging. Activity in both pathways showed predominantly local but also some long-range correlations. Using a novel approach to cluster and quantify behaviors based on continuous accelerometer and video data, we found that SPN ensembles active during specific actions were spatially closer and more correlated overall. Furthermore, similarity between different actions corresponded to the similarity between SPN ensemble patterns, irrespective of movement speed. Consistently, the accuracy of decoding behavior from SPN ensemble patterns was directly related to the dissimilarity between behavioral clusters. These results identify a predominantly local, but not spatially compact, organization of direct- and indirect-pathway SPN activity that maps action space independently of movement speed.

The central amygdala controls learning in the lateral amygdala

Experience-driven synaptic plasticity in the lateral amygdala is thought to underlie the formation of associations between sensory stimuli and an ensuing threat. However, how the central amygdala participates in such a learning process remains unclear. Here we show that PKC-δ-expressing central amygdala neurons are essential for the synaptic plasticity underlying learning in the lateral amygdala, as they convey information about the unconditioned stimulus to lateral amygdala neurons during fear conditioning.The authors show that PKC-δ-expressing neurons in the central amygdala, are essential for synaptic plasticity underlying learning in the lateral amygdala, as they convey information about unconditioned stimulus to the lateral amygdala as a teaching signal.

Fast online deconvolution of calcium imaging data

Fluorescent calcium indicators are a popular means for observing the spiking activity of large neuronal populations, but extracting the activity of each neuron from raw fluorescence calcium imaging data is a nontrivial problem. We present a fast online active set method to solve this sparse non-negative deconvolution problem. Importantly, the algorithm progresses through each time series sequentially from beginning to end, thus enabling real-time online estimation of neural activity during the imaging session. Our algorithm is a generalization of the pool adjacent violators algorithm (PAVA) for isotonic regression and inherits its linear-time computational complexity. We gain remarkable increases in processing speed: more than one order of magnitude compared to currently employed state of the art convex solvers relying on interior point methods. Our method can exploit warm starts; therefore optimizing model hyperparameters only requires a handful of passes through the data. A minor modification can further improve the quality of activity inference by imposing a constraint on the minimum spike size. The algorithm enables real-time simultaneous deconvolution of

Impact of neuronal heterogeneity on correlated colored noise-induced synchronization

O(10^5)

Anxiety Cells in a Hippocampal-Hypothalamic Circuit

traces of whole-brain larval zebrafish imaging data on a laptop.Fluorescent calcium indicators are a popular means for observing the spiking activity of large neuronal populations, but extracting the activity of each neuron from raw fluorescence calcium imaging data is a nontrivial problem. We present a fast online active set method to solve this sparse non-negative deconvolution problem. Importantly, the algorithm 3progresses through each time series sequentially from beginning to end, thus enabling real-time online estimation of neural activity during the imaging session. Our algorithm is a generalization of the pool adjacent violators algorithm (PAVA) for isotonic regression and inherits its linear-time computational complexity. We gain remarkable increases in processing speed: more than one order of magnitude compared to currently employed state of the art convex solvers relying on interior point methods. Unlike these approaches, our method can exploit warm starts; therefore optimizing model hyperparameters only requires a handful of passes through the data. A minor modification can further improve the quality of activity inference by imposing a constraint on the minimum spike size. The algorithm enables real-time simultaneous deconvolution of O(105) traces of whole-brain larval zebrafish imaging data on a laptop.

False discovery rate regression: an application to neural synchrony detection in primary visual cortex

Synchronization plays an important role in neural signal processing and transmission. Many hypotheses have been proposed to explain the origin of neural synchronization. In recent years, correlated noise-induced synchronization has received support from many theoretical and experimental studies. However, many of these prior studies have assumed that neurons have identical biophysical properties and that their inputs are well modeled by white noise. In this context, we use colored noise to induce synchronization between oscillators with heterogeneity in both phase-response curves and frequencies. In the low noise limit, we derive novel analytical theory showing that the time constant of colored noise influences correlated noise-induced synchronization and that oscillator heterogeneity can limit synchronization. Surprisingly, however, heterogeneous oscillators may synchronize better than homogeneous oscillators given low input correlations. We also find resonance of oscillator synchronization to colored noise inputs when firing frequencies diverge. Collectively, these results prove robust for both relatively high noise regimes and when applied to biophysically realistic spiking neuron models, and further match experimental recordings from acute brain slices.

Efficient and accurate extraction of in vivo calcium signals from microendoscopic video data

The hippocampus is traditionally thought to transmit contextual information to limbic structures where it acquires valence. Using freely moving calcium imaging and optogenetics, we show that while the dorsal CA1 subregion of the hippocampus is enriched in place cells, ventral CA1 (vCA1) is enriched in anxiety cells that are activated by anxiogenic environments and required for avoidance behavior. Imaging cells defined by their projection target revealed that anxiety cells were enriched in the vCA1 population projecting to the lateral hypothalamic area (LHA) but not to the basal amygdala (BA). Consistent with this selectivity, optogenetic activation of vCA1 terminals in LHA but not BA increased anxiety and avoidance, while activation of terminals in BA but not LHA impaired contextual fear memory. Thus, the hippocampus encodes not only neutral but also valence-related contextual information, and the vCA1-LHA pathway is a direct route by which the hippocampus can rapidly influence innate anxiety behavior.

Establishing a Statistical Link between Network Oscillations and Neural Synchrony.

This article introduces false discovery rate regression, a method for incorporating covariate information into large-scale multiple-testing problems. FDR regression estimates a relationship between test-level covariates and the prior probability that a given observation is a signal. It then uses this estimated relationship to inform the outcome of each test in a way that controls the overall false discovery rate at a prespecified level. This poses many subtle issues at the interface between inference and computation, and we investigate several variations of the overall approach. Simulation evidence suggests that: (1) when covariate effects are present, FDR regression improves power for a fixed false-discovery rate; and (2) when covariate effects are absent, the method is robust, in the sense that it does not lead to inflated error rates. We apply the method to neural recordings from primary visual cortex. The goal is to detect pairs of neurons that exhibit fine-time-scale interactions, in the sense that they fire together more often than expected due to chance. Our method detects roughly 50% more synchronous pairs versus a standard FDR-controlling analysis. The companion R package FDRreg implements all methods described in the article. Supplementary materials for this article are available online.

CaImAn: An open source tool for scalable Calcium Imaging data Analysis

In vivo calcium imaging through microendoscopic lenses enables imaging of previously inaccessible neuronal populations deep within the brains of freely moving animals. However, it is computationally challenging to extract single-neuronal activity from microendoscopic data, because of the very large background fluctuations and high spatial overlaps intrinsic to this recording modality. Here, we describe a new constrained matrix factorization approach to accurately separate the background and then demix and denoise the neuronal signals of interest. We compared the proposed method against previous independent components analysis and constrained nonnegative matrix factorization approaches. On both simulated and experimental data recorded from mice, our method substantially improved the quality of extracted cellular signals and detected more well-isolated neural signals, especially in noisy data regimes. These advances can in turn significantly enhance the statistical power of downstream analyses, and ultimately improve scientific conclusions derived from microendoscopic data.

Penalized matrix decomposition for denoising, compression, and improved demixing of functional imaging data

Pairs of active neurons frequently fire action potentials or “spikes” nearly synchronously (i.e., within 5 ms of each other). This spike synchrony may occur by chance, based solely on the neurons’ fluctuating firing patterns, or it may occur too frequently to be explicable by chance alone. When spike synchrony above chances levels is present, it may subserve computation for a specific cognitive process, or it could be an irrelevant byproduct of such computation. Either way, spike synchrony is a feature of neural data that should be explained. A point process regression framework has been developed previously for this purpose, using generalized linear models (GLMs). In this framework, the observed number of synchronous spikes is compared to the number predicted by chance under varying assumptions about the factors that affect each of the individual neuron’s firing-rate functions. An important possible source of spike synchrony is network-wide oscillations, which may provide an essential mechanism of network information flow. To establish the statistical link between spike synchrony and network-wide oscillations, we have integrated oscillatory field potentials into our point process regression framework. We first extended a previously-published model of spike-field association and showed that we could recover phase relationships between oscillatory field potentials and firing rates. We then used this new framework to demonstrate the statistical relationship between oscillatory field potentials and spike synchrony in: 1) simulated neurons, 2) in vitro recordings of hippocampal CA1 pyramidal cells, and 3) in vivo recordings of neocortical V4 neurons. Our results provide a rigorous method for establishing a statistical link between network oscillations and neural synchrony.