Biology subjects

Harris, K. D.

Publications and source records attributed to Harris, K. D..

15 recordsLinked to original sources

bioRxiv2018

A tool for analyzing electrode tracks from slice histology

It is now possible to record from hundreds of neurons across multiple brain regions in a single electrophysiology experiment. An essential step in the ensuing data analysis is to assign recorded neurons to the correct brain regions. Brain regions are typically identified after the recordings by comparing images of brain slices to a reference atlas by eye. This introduces error, in particular when slices are not cut at a perfectly coronal angle or when electrode tracks span multiple slices. Here we introduce SHARP-Track, a tool to localize regions of interest and plot the brain regions they pass through. SHARP-Track offers a MATLAB user interface to explore the Allen Mouse Brain Atlas, register asymmetric slice images to the atlas using manual input, and interactively analyze electrode tracks. We find that it reduces error compared to localizing electrodes in a reference atlas by eye. See github.com/cortex-lab/allenCCF for the software and wiki.

neuroscience↗

bioRxiv2018

A spatial atlas of inhibitory cell types in mouse hippocampus

Understanding the function of a tissue requires knowing the spatial organization of its constituent cell types. In the cerebral cortex, single-cell RNA sequencing (scRNA-seq) has revealed the genome-wide expression patterns that define its many, closely related cell types, but cannot reveal their spatial arrangement. Here we introduce probabilistic cell typing by in situ sequencing (pciSeq), an approach that leverages prior scRNA-seq classification to identify cell types using multiplexed in situ RNA detection. We applied this method to map the inhibitory neurons of hippocampal area CA1, a cell system critical for memory function, for which ground truth is available from extensive prior work identifying the laminar organization of subtly differing cell types. Our method confidently identified 16 interneuron classes, in a spatial arrangement closely matching ground truth. This method will allow identifying the spatial organization of fine cell types across the brain and other tissues.

neuroscience↗

bioRxiv2018

Dopaminergic and frontal signals for decisions guided by sensory evidence and reward value

Making efficient decisions requires combining present sensory evidence with previous reward values, and learning from the resulting outcome. To establish the underlying neural processes, we trained mice in a task that probed such decisions. Mouse choices conformed to a reinforcement learning model that estimates predicted value (reward value times sensory confidence) and prediction error (outcome minus predicted value). Predicted value was encoded in the pre-outcome activity of prelimbic frontal neurons and midbrain dopamine neurons. Prediction error was encoded in the post-outcome activity of dopamine neurons, which reflected not only reward value but also sensory confidence. Manipulations of these signals spared ongoing choices but profoundly affected subsequent learning. Learning depended on the pre-outcome activity of prelimbic neurons, but not dopamine neurons. Learning also depended on the post-outcome activity of dopamine neurons, but not prelimbic neurons. These results reveal the distinct roles of frontal and dopamine neurons in learning under uncertainty.

neuroscience↗

bioRxiv2018

Distinct structure of cortical population activity on fast and infraslow timescales

Cortical activity is organised across multiple spatial and temporal scales. Most research on the dynamics of neuronal spiking is concerned with timescales of 1 ms - 1 s, and little is known about spiking dynamics on timescales of tens of seconds and minutes. Here, we used frequency domain analyses to study the structure of individual neurons spiking activity and its coupling to local population rate and to arousal level across frequencies ranging from 0.01 to 100 Hz. In mouse medial prefrontal cortex (mPFC), the spiking dynamics of individual neurons could be quantitatively captured by a combination of interspike interval and firing rate power spectrum distributions. The relative strength of coherence with local population often differed across timescales: a neuron strongly coupled to population rate on fast timescales could be weakly coupled on slow timescales, and vice versa. On slow but not fast timescales, a substantial proportion of neurons showed firing anti-correlated with the population. Infraslow firing rate changes were largely determined by arousal rather than by local factors, which could explain the timescale dependence of population coupling strength of individual neurons. These observations demonstrate how individual neurons simultaneously partake in fast local dynamics, and slow brain-wide dynamics, extending our understanding of infraslow cortical activity beyond the mesoscale resolution of fMRI studies.

neuroscience↗

bioRxiv2018

High-dimensional geometry of population responses in visual cortex

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 nth 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.

neuroscience↗

bioRxiv2018

Cortical state fluctuations during sensory decision making

Neocortical activity varies between states of \"synchronization\" and \"desynchronization\", with desynchronized states believed to occur specifically in regions engaged by the task. To disambiguate whether desynchronization is linked to task performance or engagement, we trained mice on tasks in which incorrect responses due to disengagement (neglect) differed from inaccurate task performance (incorrect choices). Using widefield calcium imaging to measure cortical state across many areas simultaneously, we found that desynchronization was correlated with engagement rather than accuracy. Consistent with this link between desynchronization and engagement, we found that rewards had a long-lasting desynchronizing effect. To determine whether engagement-related changes in cortical state depended on the sensory modality, we trained mice on visual and auditory task versions and found that desynchronization was similar in both and more pronounced in somatomotor than either sensory cortex. We conclude that variations in cortical state are predominately global and closely relate to variations in task engagement.

neuroscience↗

bioRxiv2018

Spontaneous behaviors drive multidimensional, brain-wide population activity

Cortical responses to sensory stimuli are highly variable, and sensory cortex exhibits intricate spontaneous activity even without external sensory input. Cortical variability and spontaneous activity have been variously proposed to represent random noise, recall of prior experience, or encoding of ongoing behavioral and cognitive variables. Here, by recording over 10,000 neurons in mouse visual cortex, we show that spontaneous activity reliably encodes a high-dimensional latent state, which is partially related to the mouses ongoing behavior and is represented not just in visual cortex but across the forebrain. Sensory inputs do not interrupt this ongoing signal, but add onto it a representation of visual stimuli in orthogonal dimensions. Thus, visual cortical population activity, despite its apparently noisy structure, reliably encodes an orthogonal fusion of sensory and multidimensional behavioral information.

neuroscience↗

bioRxiv2018

Molecular architecture of the mouse nervous system

The mammalian nervous system executes complex behaviors controlled by specialised, precisely positioned and interacting cell types. Here, we used RNA sequencing of half a million single cells to create a detailed census of cell types in the mouse nervous system. We mapped cell types spatially and derived a hierarchical, data-driven taxonomy. Neurons were the most diverse, and were grouped by developmental anatomical units, and by the expression of neurotransmitters and neuropeptides. Neuronal diversity was driven by genes encoding cell identity, synaptic connectivity, neurotransmission and membrane conductance. We discovered several distinct, regionally restricted, astrocytes types, which obeyed developmental boundaries and correlated with the spatial distribution of key glutamate and glycine neurotransmitters. In contrast, oligodendrocytes showed a loss of regional identity, followed by a secondary diversification. The resource presented here lays a solid foundation for understanding the molecular architecture of the mammalian nervous system, and enables genetic manipulation of specific cell types.

neuroscience↗

bioRxiv2018

High resolution data-driven model of the mouse connectome

Knowledge of mesoscopic brain connectivity is important for understanding inter- and intra-region information processing. Models of structural connectivity are typically constructed and analyzed with the assumption that regions are homogeneous. We instead use the Allen Mouse Brain Connectivity Atlas to construct a model of whole brain connectivity at the scale of 100 {micro}m voxels. The dataset used consists of 366 anterograde tracing experiments in wild type C7BL/6 mice, mapping fluorescently-labeled neuronal projections brain-wide. Inferring spatial connectivity with this dataset remains underdetermined, since the approximately 2 x 105 source voxels outnumber the number of experiments. To address this, we assume that connection patterns and strengths vary smoothly across major brain divisions. We model the connectivity at each voxel as a radial basis kernel-weighted average of the projection patterns of nearby injections. The voxel model outperforms a previous regional model in predicting held-out experiments and compared to a human-curated dataset. This voxel-scale model of the mouse connectome permits researchers to extend their previous analyses of structural connectivity to unprecedented levels of resolution, and allows for comparison with functional imaging and other datasets.

neuroscience↗

bioRxiv2017

Coherent representations of subjective spatial position in primary visual cortex and hippocampus

A major role of vision is to guide navigation, and navigation is strongly driven by vision1-4. Indeed, the brains visual and navigational systems are known to interact5, 6, and signals related to position in the environment have been suggested to appear as early as in visual cortex6, 7. To establish the nature of these signals we recorded in primary visual cortex (V1) and in the CA1 region of the hippocampus while mice traversed a corridor in virtual reality. The corridor contained identical visual landmarks in two positions, so that a purely visual neuron would respond similarly in those positions. Most V1 neurons, however, responded solely or more strongly to the landmarks in one position. This modulation of visual responses by spatial location was not explained by factors such as running speed. To assess whether the modulation is related to navigational signals and to the animals subjective estimate of position, we trained the mice to lick for a water reward upon reaching a reward zone in the corridor. Neuronal populations in both CA1 and V1 encoded the animals position along the corridor, and the errors in their representations were correlated. Moreover, both representations reflected the animals subjective estimate of position, inferred from the animals licks, better than its actual position. Indeed, when animals licked in a given location - whether correct or incorrect - neural populations in both V1 and CA1 placed the animal in the reward zone. We conclude that visual responses in V1 are tightly controlled by navigational signals, which are coherent with those encoded in hippocampus, and reflect the animals subjective position in the environment. The presence of such navigational signals as early as in a primary sensory area suggests that these signals permeate sensory processing in the cortex.

neuroscience↗

bioRxiv2017

Community-based benchmarking improves spike inference from two-photon calcium imaging data

In recent years, two-photon calcium imaging has become a standard tool to probe the function of neural circuits and to study computations in neuronal populations1, 2. However, the acquired signal is only an indirect measurement of neural activity due to the comparatively slow dynamics of fluorescent calcium indicators3. Different algorithms for estimating spike trains from noisy calcium measurements have been proposed in the past4-8, but it is an open question how far performance can be improved. Here, we report the results of the spikefinder challenge, launched to catalyze the development of new spike inference algorithms through crowd-sourcing. We present ten of the submitted algorithms which show improved performance compared to previously evaluated methods. Interestingly, the top-performing algorithms are based on a wide range of principles from deep neural networks to generative models, yet provide highly correlated estimates of the neural activity. The competition shows that benchmark challenges can drive algorithmic developments in neuroscience.

neuroscience↗

bioRxiv2017

Decision and navigation in mouse parietal cortex

Posterior parietal cortex (PPC) has been implicated in navigation, in the control of body movement, and in visually-guided decisions. Seeking to relate these views, we measured activity in populations of PPC neurons while mice performed a virtual navigation task driven by visual decisions. We found that PPC neurons are selective for specific combinations of the animals spatial position and heading angle. This selectivity closely predicted both the activity of individual PPC neurons, and the arrangement of their collective firing patterns in choice-selective sequences. These sequences reflected the influence on PPC of the animals navigation trajectory. An alternative model in which PPC activity encoded position and decision performed more poorly, and a model with all three variables barely outperformed the simple position-heading model. Alternative models based on visual or motor variables were inferior. We conclude that when mice use vision to choose their trajectories, a large fraction of parietal cortex activity can be predicted from simple attributes such as spatial position and heading.

neuroscience↗

bioRxiv2017

Robustness of spike deconvolution for calcium imaging of neural spiking

Calcium imaging is a powerful method to record the activity of neural populations, but inferring spike times from calcium signals is a challenging problem. We compared multiple approaches using multiple datasets with ground truth electrophysiology, and found that simple non-negative deconvolution (NND) outperformed all other algorithms. We introduce a novel benchmark applicable to recordings without electrophysiological ground truth, based on the correlation of responses to two stimulus repeats, and used this to show that unconstrained NND also outperformed the other algorithms when run on \"zoomed out\" datasets of ~10,000 cell recordings. Finally, we show that NND-based methods match the performance of a supervised method based on convolutional neural networks, while avoiding some of the biases of such methods, and at much faster running times. We therefore recommend that spikes be inferred from calcium traces using simple NND, due to its simplicity, efficiency and accuracy.

neuroscience↗

bioRxiv2017

Single-Cell Transcriptomic Analysis Of CA1 Inhibitory Neurons

Understanding any brain circuit will require a categorization of its constituent neurons. In hippocampal area CA1, at least 23 classes of GABAergic neuron have been proposed to date. However, this list may be incomplete; additionally, it is unclear whether discrete classes are sufficient to describe the diversity of cortical inhibitory neurons, or whether continuous modes of variability are also required. We studied the transcriptomes of 3663 CA1 inhibitory cells, revealing 10 major GABAergic groups that divided into 49 fine-scale clusters. All previously described and several novel cell classes were identified, with three previously-described classes unexpectedly found to be identical. A division into discrete classes however was not sufficient to describe the diversity of these cells, as continuous variation also occurred between and within classes. Latent factor analysis revealed that a single continuous variable could predict the expression levels of several genes, which correlated similarly with it across multiple cell types. Analysis of the genes correlating with this variable suggested it reflects a range from metabolically highly active faster-spiking cells that proximally target pyramidal cells, to slower-spiking cells targeting distal dendrites or interneurons. These results elucidate the complexity of inhibitory neurons in one of the simplest cortical structures, and show that characterizing these cells requires continuous modes of variation as well as discrete cell classes.

neuroscience↗

bioRxiv2017

Aberrant Cortical Activity In Multiple GCaMP6-Expressing Transgenic Mouse Lines

Transgenic mouse lines are invaluable tools for neuroscience but as with any technique, care must be taken to ensure that the tool itself does not unduly affect the system under study. Here we report aberrant electrical activity, similar to interictal spikes, and accompanying fluorescence events in some genotypes of transgenic mice expressing GCaMP6 genetically-encoded calcium sensors. These epileptiform events have been observed particularly, but not exclusively, in mice with Emx1-Cre and Ai93 transgenes, across multiple laboratories. The events occur at >0.1 Hz, are very large in amplitude (>1.0 mV local field potentials, >10% df/f widefield imaging signals), and typically cover large regions of cortex. Many properties of neuronal responses and behavior seem normal despite these events, though rare subjects exhibit overt generalized seizures. The underlying mechanisms of this phenomenon remain unclear, but we speculate about possible causes on the basis of diverse observations. We encourage researchers to be aware of these activity patterns while interpreting neuronal recordings from affected mouse lines and when considering which lines to study.

neuroscience↗