Biology subjects

Harris, K. D.

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

At least 55 records · Page 3Linked to original sources

bioRxiv2021

Structured random receptive fields enable informative sensory encodings

Brains must represent the outside world so that animals survive and thrive. In early sensory systems, neural populations have diverse receptive fields structured to detect important features in inputs, yet significant variability has been ignored in classical models of sensory neurons. We model neuronal receptive fields as random, variable samples from parameterized distributions and demonstrate this model in two sensory modalities using data from insect mechanosensors and mammalian primary visual cortex. Our approach leads to a significant theoretical connection between the foundational concepts of receptive fields and random features, a leading theory for understanding artificial neural networks. The modeled neurons perform a randomized wavelet transform on inputs, which removes high frequency noise and boosts the signal. Further, these random feature neurons enable learning from fewer training samples and with smaller networks in artificial tasks. This structured random model of receptive fields provides a unifying, mathematically tractable framework to understand sensory encodings across both spatial and temporal domains.

neuroscience↗

bioRxiv2021

Behavioral origin of sound-evoked activity in visual cortex

Sensory cortices can be affected by stimuli of multiple modalities and are thus increasingly thought to be multisensory. For instance, primary visual cortex (V1) is influenced not only by images but also by sounds. Here we show that the activity evoked by sounds in V1 is highly stereotyped across neurons and even across mice. It resembles activity measured elsewhere in the brain and is independent of projections from auditory cortex. Its low-dimensional nature starkly contrasts the high dimensional code that V1 uses to represent images. Furthermore, this sound-evoked activity can be precisely predicted by small body movements that are elicited by each sound and are highly stereotyped across trials and across mice. Thus, neural activity that is apparently multisensory may simply arise from low-dimensional signals associated with changes in internal state and behavior.

neuroscience↗

bioRxiv2021

Disruption of VGLUT1 in cholinergic medial habenula projections increases nicotine self-administration

Cholinergic projections from the medial habenula (MHb) to the interpeduncular nucleus (IPN) have been studied for their complex contributions to nicotine addiction and have been implicated in nicotine reinforcement, aversion, and withdrawal. While it has been established that MHb cholinergic projections co-release glutamate, no direct evidence has demonstrated a role for this specific glutamate projection in nicotine consumption. In the present study, a novel floxed Slc17a7 (VGLUT1) mouse was generated and used to create conditional knockout (cKO) mice that lack VGLUT1 in MHb cholinergic neurons. Histochemical approaches and optogenetics-assisted electrophysiology were used to validate the disruption of VGLUT1 from cholinergic MHb to IPN projections. The mice displayed no gross phenotypic abnormalities and exhibited normal exploratory and locomotor behavior in the open-field assay. However, the loss of VGLUT1-mediated glutamate co-release led to increased nicotine self-administration. These findings indicate that glutamate co-release from ventral MHb cholinergic neurons opposes nicotine consumption and provide additional support for targeting this synapse to develop potential treatments to nicotine addiction.

neuroscience↗

bioRxiv2021

Learning orthogonalizes visual cortical population codes

Training in behavioral tasks can alter visual cortical stimulus coding, but the precise form of this plasticity is unclear. We measured orientation tuning in 4,000-neuron populations of mouse V1 before and after training on a visuomotor task. Changes to single-cell tuning curves were apparently complex, including appearance of asymmetric and multi-peaked tuning curves. Nevertheless, these changes reflected a simple mathematical transformation of population activity, suppressing responses to motor-associated stimuli specifically in cells responding at intermediate levels. The strength of the transformation varied across trials, suggesting a dynamic circuit mechanism rather than static synaptic plasticity. This transformation resulted in sparsening and orthogonalization of population codes for motor-associated stimuli. Training did not improve the performance of an optimal stimulus decoder, which was already perfect even for naive codes, but the resulting orthogonalization improved the performance of a suboptimal decoder model with inductive bias as might be found in downstream readout circuits.

neuroscience↗

bioRxiv2021

The role of frontal cortex in multisensory decisions

To make accurate perceptual decisions, the brain often combines information across sensory modalities. For instance, localizing objects by integrating their image and sound. However, the cortical substrates underlying this audiovisual integration remain uncertain. Here, we show that mouse frontal cortex combines auditory and visual evidence; that this combination is additive, mirroring behavior; and that it evolves with learning. Scanning optogenetic inactivation demonstrated that inactivating frontal cortex impaired choices based on either sensory modality. Recordings from >10,000 neurons indicated that after task learning, activity in frontal area MOs (secondary motor cortex) encodes an additive combination of visual and auditory signals, consistent with the mices behavioral strategy. An accumulator model applied to these sensory representations reproduced both the observed choices and reaction times. These results indicate that frontal cortex adapts through learning to combine evidence across sensory cortices, providing a signal that is transformed into a binary decision by a downstream accumulator.

neuroscience↗

bioRxiv2021

Neural basis of functional ultrasound signals

Functional ultrasound imaging (fUSI) is a popular method for measuring blood flow and thus infer brain activity, but it relies on the physiology of neurovascular coupling and requires extensive signal processing. To establish to what degree its trial-by-trial signals reflect neural activity, we performed simultaneous fUSI and neural recordings with Neuropixels probes in awake mice. fUSI signals strongly correlated with the slow (<0.3 Hz) fluctuations in local firing rate, and were closely predicted by the smoothed firing rate of local neurons, particularly putative inhibitory neurons. The optimal smoothing filter had width ~3 s, matched the hemodynamic response function of awake mouse, was invariant across mice and stimulus conditions, and similar in cortex and hippocampus. fUSI signals also matched neural firing spatially: firing rates were as highly correlated across hemispheres as fUSI signals. Thus, hemodynamic signals measured by ultrasound bear a simple and accurate relationship to neuronal firing.

neuroscience↗

bioRxiv2020

Task specificity in mouse parietal cortex

Parietal cortex is implicated in a variety of behavioral processes, but it is unknown whether and how individual neurons participate in multiple tasks. We trained head-fixed mice to perform two visual decision tasks involving a steering wheel or a virtual T-maze, and recorded from the same parietal neurons during the two. Neurons that were active during the T-maze task were typically inactive during the steering-wheel task, and vice versa. Recording from the same neurons in the same apparatus without task stimuli yielded the same specificity as in the task, suggesting that task specificity depends on physical context. To confirm this, we trained some mice in a third task combining the steering wheel with the visual environment of the T-maze. This hybrid task engaged the same neurons as the steering-wheel task. Thus, participation by neurons in mouse parietal cortex is task-specific, and this specificity is determined by physical context.

neuroscience↗

bioRxiv2020

Nonsense correlations in neuroscience

Many neurophysiological signals exhibit slow continuous trends over time. Because standard correlation analyses assume that all samples are independent, they can yield apparently significant "nonsense correlations" even for signals that are completely unrelated. Here we compare the performance of several methods for assessing correlations between timeseries, using simulated slowly drifting signals with and without genuine correlations. The best performance was obtained from a "pseudosession method", which relies on one of the signals being randomly generated by the experimenter, or a "session perturbation" method which requires multiple recordings under the same conditions. If neither of these is applicable, a "linear shift" method can be used when one of the signals is stationary. Methods based on cross-validation, circular shifting, phase randomization, or detrending gave up to 100% false positive rates in our simulations. We conclude that analysis of neural timeseries is best performed when stationarity and randomization is built into the experimental design.

neuroscience↗

bioRxiv2020

Neuropixels 2.0: A miniaturized high-density probe for stable, long-term brain recordings

To study the dynamics of neural processing across timescales, we require the ability to follow the spiking of thousands of individually separable neurons over weeks and months, during unrestrained behavior. To address this need, we introduce the Neuropixels 2.0 probe together with novel analysis algorithms. The new probe has over 5,000 sites and is miniaturized such that two probes plus a headstage, recording 768 sites at once, weigh just over 1 g, suitable for implanting chronically in small mammals. Recordings with high quality signals persisting for at least two months were reliably obtained in two species and six different labs. Improved site density and arrangement combined with new data processing methods enable automatic post-hoc stabilization of data despite brain movements during behavior and across days, allowing recording from the same neurons in the mouse visual cortex for over 2 months. Additionally, an optional configuration allows for recording from multiple sites per available channel, with a penalty to signal-to-noise ratio. These probes and algorithms enable stable recordings from >10,000 sites during free behavior in small animals such as mice.

neuroscience↗

bioRxiv2020

Dopamine axons to dorsal striatum encode contralateral stimuli and actions

Midbrain dopamine neurons play key roles in decision-making by regulating reward valuation and actions. These roles are thought to depend on dopamine neurons innervating striatum. In addition to actions and rewards, however, efficient decisions often involve consideration of uncertain sensory signals. The functions of striatal dopamine during sensory decisions remains unknown. We trained mice in a task that probed decisions based on sensory evidence and reward value, and recorded the activity of striatal dopamine axons. Dopamine axons in ventral striatum (VS) responded to bilateral stimuli and trial outcomes, encoding prediction errors that scaled with decision confidence and reward value. By contrast, dopamine axons in dorsal striatum (DS) responded to contralateral stimuli and contralateral actions. Thus, during sensory decisions, striatal dopamine signals are anatomically organized. VS dopamine resembles prediction errors suitable for reward maximization under sensory uncertainty whereas DS dopamine encodes specific combinations of stimuli and actions in a lateralized fashion.

neuroscience↗

bioRxiv2020

Joint nonparametric coalescent inference of mutation spectrum history and demography

Booming and busting populations modulate the accumulation of genetic diversity, encoding histories of living populations in present-day variation. Many methods exist to decode these histories, and all must make strong model assumptions. It is typical to assume that mutations accumulate uniformly across the genome at a constant rate that does not vary between closely related populations. However, recent work shows that mutational processes in human and great ape populations vary across genomic regions and evolve over time. This perturbs the mutation spectrum: the relative mutation rates in different local nucleotide contexts. Here, we develop theoretical tools in the framework of Kingmans coalescent to accommodate mutation spectrum dynamics. We describe mushi: a method to perform fast, nonparametric joint inference of demographic and mutation spectrum histories from allele frequency data. We use mushi to reconstruct trajectories of effective population size and mutation spectrum divergence between human populations, identify mutation signatures and their dynamics in different human populations, and produce more accurate time calibration for a previously-reported mutational pulse in the ancestors of Europeans. We show that mutation spectrum histories can be productively incorporated in a well-studied theoretical setting, and rigorously inferred from genomic variation data like other features of evolutionary history.

evolutionary biology↗

bioRxiv2020

Equations governing dynamics of excitation and inhibition in the mouse corticothalamic network

Although cortical circuits are complex and interconnected with the rest of the brain, their macroscopic dynamics are often approximated by modeling the averaged activities of excitatory and inhibitory cortical neurons, without interactions with other brain circuits. To verify the validity of such mean-field models, we optogenetically stimulated populations of excitatory and parvalbumin-expressing inhibitory neurons in awake mouse visual cortex, while recording population activity in cortex and in its thalamic correspondent, the lateral geniculate nucleus. The cortical responses to brief test pulses could not be explained by a mean-field model including only cortical excitatory and inhibitory populations. However, these responses could be predicted by extending the model to include thalamic interactions that cause net cortical suppression following activation of cortical excitatory neurons. We conclude that mean-field models can accurately summarize cortical dynamics, but only when the cortex is considered as part of a dynamic corticothalamic network.

neuroscience↗

bioRxiv2020

Processive and stochastic CHH methylation by plant DRM2 and CMT2 revealed by single-read methylome analysis

Cytosine methylome data is commonly generated through next-generation sequencing (NGS). Analyses of this data average methylation states of individual reads. We propose an alternate method of analysing single-read methylome data. Using this method, we identified patterns that relate to the mechanism of two plant non-CG methylating enzymes, DRM2 and CMT2: DRM2 has higher processivity than CMT2, and DRM2-methylated regions have higher variation among cells. Based on these patterns, we developed a classifier that predicts enzyme activity in different species and tissues. To facilitate further single-read analyses, we developed a genome browser optimised for visualising and analysing NGS data at single-read resolution.

plant biology↗

bioRxiv2019

Spatial encoding in the visual pathway arises in cortex and depends on active navigation

During navigation, the visual responses of neurons in primary visual cortex (V1) are modulated by the animals spatial position. Here we show that this spatial modulation is similarly present across multiple higher visual areas but largely absent in the main thalamic pathway into V1. Similar to hippocampus, spatial modulation in visual cortex strengthens with experience and requires engagement in active behavior. Active navigation in a familiar environment, therefore, determines spatial modulation of visual signals starting in the cortex.

neuroscience↗

bioRxiv2019

Data architecture and visualization for a large-scale neuroscience collaboration

Effective data management is a major challenge for neuroscience labs, and even greater for collaborative projects. In the International Brain laboratory (IBL), ten experimental labs spanning 7 geographically distributed sites measure neural activity across the brains of mice making perceptual decisions. Here, we report a novel, modular architecture that allows users to contribute, access, and analyze data across this collaboration. Users contribute data using a web-based electronic lab notebook (Alyx), which automatically registers recorded data files and uploads them to a central server. Users access data with a lightweight interface, the Open Neurophysiology Environment (ONE), which searches data from all labs and loads it into MATLAB or Python. To analyze data, we have developed pipelines based on DataJoint, which automatically populate a website displaying a graphical summary of results to date. This architecture provides a new framework to contribute, access and analyze data, surmounting many challenges currently faced by neuroscientists.

neuroscience↗

bioRxiv2019

Striatal activity reflects cortical activity patterns

The dorsal striatum is organized into domains that drive characteristic behaviors1-7, and receive inputs from different parts of the cortex8,9 which modulate similar behaviors10-12. Striatal responses to cortical inputs, however, can be affected by changes in connection strength13-15, local striatal circuitry16,17, and thalamic inputs18,19. Therefore, it is unclear whether the pattern of activity across striatal domains mirrors that across the cortex20-23 or differs from it24-28. Here we use simultaneous large-scale recordings in the cortex and the striatum to show that striatal activity can be accurately predicted by spatiotemporal activity patterns in the cortex. The relationship between activity in the cortex and the striatum was spatially consistent with corticostriatal anatomy, and temporally consistent with a feedforward drive. Each striatal domain exhibited specific sensorimotor responses that predictably followed activity in the associated cortical regions, and the corticostriatal relationship remained unvaried during passive states or performance of a task probing visually guided behavior. However, the tasks visual stimuli and corresponding behavioral responses evoked relatively more activity in the striatum than in associated cortical regions. This increased striatal activity involved an additive offset in firing rate, which was independent of task engagement but only present in animals that had learned the task. Thus, striatal activity largely reflects patterns of cortical activity, deviating from them in a simple additive fashion for learned stimuli or actions.

neuroscience↗

bioRxiv2019

RIGBOX: AN OPEN-SOURCE TOOLBOX FOR PROBING NEURONS AND BEHAVIOR

Setting up an experiment in behavioral neuroscience is a complex process that is often managed with ad hoc solutions. To streamline this process we developed Rigbox, a high-performance, open-source software toolbox that facilitates a modular approach to designing experiments (github.com/cortex-lab/Rigbox). Rigbox simplifies hardware I/O, synchronizes data streams from multiple sources, communicates with remote databases, and implements visual and auditory stimuli presentation. Its main submodule, Signals, allows intuitive programming of behavioral tasks. Here we illustrate its function with two interactive examples: a human psychophysics experiment, and the game of Pong. We give an overview of the other packages in Rigbox, provide benchmarks, and conclude with a discussion on the extensibility of the software and comparisons with similar toolboxes. Rigbox runs in MATLAB, with Java components to handle network communication, and a C library to boost performance.

neuroscience↗

bioRxiv2019

Retinal outputs depend on behavioural state

The operating mode of the visual system depends on behavioural states such as arousal1,2. This dependence is seen both in primary visual cortex3-7 (V1) and in subcortical brain structures receiving direct retinal input4,8. Here we show that this effect arises as early as in the output of the retina. We first measured activity in a region that receives retinal projections9, the superficial superior colliculus (sSC), and found that this activity strongly depended on behavioural state. This modulation was not mediated by feedback inputs from V1 as it was immune to V1 inactivation. We then used Neuropixels probes10 to record activity in the optic tract, and we found some retinal axons whose activity significantly varied with arousal, even in darkness. To characterize these effects on a larger sample of retinal outputs, we imaged the activity of retinal boutons11,12 in sSC during behaviour using a calcium indicator. The activity of these boutons correlated with arousal as strongly as that of sSC neurons, and this correlation persisted also during darkness. These results reveal a novel property of retinal function in mice, which could be observed only during behaviour: retinal outputs are modulated by behavioural state before they reach the rest of the brain.

neuroscience↗