Striatal activity topographically reflects cortical activity.

The cortex projects to the dorsal striatum topographically1,2 to regulate behaviour3-5, but spiking activity in the two structures has previously been reported to have markedly different relations to sensorimotor events6-9. Here we show that the relationship between activity in the cortex and striatum is spatiotemporally precise, topographic, causal and invariant to behaviour. We simultaneously recorded activity across large regions of the cortex and across the width of the dorsal striatum in mice that performed a visually guided task. Striatal activity followed a mediolateral gradient in which behavioural correlates progressed from visual cue to response movement to reward licking. The summed activity in each part of the striatum closely and specifically mirrored activity in topographically associated cortical regions, regardless of task engagement. This relationship held for medium spiny neurons and fast-spiking interneurons, whereas the activity of tonically active neurons differed from cortical activity with stereotypical responses to sensory or reward events. Inactivation of the visual cortex abolished striatal responses to visual stimuli, supporting a causal role of cortical inputs in driving the striatum. Striatal visual responses were larger in trained mice than untrained mice, with no corresponding change in overall activity in the visual cortex. Striatal activity therefore reflects a consistent, causal and scalable topographical mapping of cortical activity.

A modular architecture for organizing, processing and sharing neurophysiology data.

We describe an architecture for organizing, integrating and sharing neurophysiology data within a single laboratory or across a group of collaborators. It comprises a database linking data files to metadata and electronic laboratory notes; a module collecting data from multiple laboratories into one location; a protocol for searching and sharing data and a module for automatic analyses that populates a website. These modules can be used together or individually, by single laboratories or worldwide collaborations.

Distributed coding of choice, action and engagement across the mouse brain.

Vision, choice, action and behavioural engagement arise from neuronal activity that may be distributed across brain regions. Here we delineate the spatial distribution of neurons underlying these processes. We used Neuropixels probes1,2 to record from approximately 30,000 neurons in 42 brain regions of mice performing a visual discrimination task3. Neurons in nearly all regions responded non-specifically when the mouse initiated an action. By contrast, neurons encoding visual stimuli and upcoming choices occupied restricted regions in the neocortex, basal ganglia and midbrain. Choice signals were rare and emerged with indistinguishable timing across regions. Midbrain neurons were activated before contralateral choices and were suppressed before ipsilateral choices, whereas forebrain neurons could prefer either side. Brain-wide pre-stimulus activity predicted engagement in individual trials and in the overall task, with enhanced subcortical but suppressed neocortical activity during engagement. These results reveal organizing principles for the distribution of neurons encoding behaviourally relevant variables across the mouse brain.

The Importance of Accounting for Movement When Relating Neuronal Activity to Sensory and Cognitive Processes.

A surprising finding of recent studies in mouse is the dominance of widespread movement-related activity throughout the brain, including in early sensory areas. In awake subjects, failing to account for movement risks misattributing movement-related activity to other (e.g., sensory or cognitive) processes. In this article, we (1) review task designs for separating task-related and movement-related activity, (2) review three "case studies" in which not considering movement would have resulted in critically different interpretations of neuronal function, and (3) discuss functional couplings that may prevent us from ever fully isolating sensory, motor, and cognitive-related activity. Our main thesis is that neural signals related to movement are ubiquitous, and therefore ought to be considered first and foremost when attempting to correlate neuronal activity with task-related processes.

Fully integrated silicon probes for high-density recording of neural activity.

Sensory, motor and cognitive operations involve the coordinated action of large neuronal populations across multiple brain regions in both superficial and deep structures. Existing extracellular probes record neural activity with excellent spatial and temporal (sub-millisecond) resolution, but from only a few dozen neurons per shank. Optical Ca2+ imaging offers more coverage but lacks the temporal resolution needed to distinguish individual spikes reliably and does not measure local field potentials. Until now, no technology compatible with use in unrestrained animals has combined high spatiotemporal resolution with large volume coverage. Here we design, fabricate and test a new silicon probe known as Neuropixels to meet this need. Each probe has 384 recording channels that can programmably address 960 complementary metal-oxide-semiconductor (CMOS) processing-compatible low-impedance TiN sites that tile a single 10-mm long, 70 × 20-µm cross-section shank. The 6 × 9-mm probe base is fabricated with the shank on a single chip. Voltage signals are filtered, amplified, multiplexed and digitized on the base, allowing the direct transmission of noise-free digital data from the probe. The combination of dense recording sites and high channel count yielded well-isolated spiking activity from hundreds of neurons per probe implanted in mice and rats. Using two probes, more than 700 well-isolated single neurons were recorded simultaneously from five brain structures in an awake mouse. The fully integrated functionality and small size of Neuropixels probes allowed large populations of neurons from several brain structures to be recorded in freely moving animals. This combination of high-performance electrode technology and scalable chip fabrication methods opens a path towards recording of brain-wide neural activity during behaviour.

Laminar segregation of sensory coding and behavioral readout in macaque V4.

Neurons in sensory areas of the neocortex are known to represent information both about sensory stimuli and behavioral state, but how these 2 disparate signals are integrated across cortical layers is poorly understood. To study this issue, we measured the coding of visual stimulus orientation and of behavioral state by neurons within superficial and deep layers of area V4 in monkeys while they covertly attended or prepared eye movements to visual stimuli. We show that whereas single neurons and neuronal populations in the superficial layers conveyed more information about the orientation of visual stimuli than neurons in deep layers, the opposite was true of information about the behavioral relevance of those stimuli. In particular, deep layer neurons encoded greater information about the direction of planned eye movements than superficial neurons. These results suggest a division of labor between cortical layers in the coding of visual input and visually guided behavior.

Visual space is compressed in prefrontal cortex before eye movements.

We experience the visual world through a series of saccadic eye movements, each one shifting our gaze to bring objects of interest to the fovea for further processing. Although such movements lead to frequent and substantial displacements of the retinal image, these displacements go unnoticed. It is widely assumed that a primary mechanism underlying this apparent stability is an anticipatory shifting of visual receptive fields (RFs) from their presaccadic to their postsaccadic locations before movement onset. Evidence of this predictive 'remapping' of RFs has been particularly apparent within brain structures involved in gaze control. However, critically absent among that evidence are detailed measurements of visual RFs before movement onset. Here we show that during saccade preparation, rather than remap, RFs of neurons in a prefrontal gaze control area massively converge towards the saccadic target. We mapped the visual RFs of prefrontal neurons during stable fixation and immediately before the onset of eye movements, using multi-electrode recordings in monkeys. Following movements from an initial fixation point to a target, RFs remained stationary in retinocentric space. However, in the period immediately before movement onset, RFs shifted by as much as 18 degrees of visual angle, and converged towards the target location. This convergence resulted in a threefold increase in the proportion of RFs responding to stimuli near the target region. In addition, like in human observers, the population of prefrontal neurons grossly mislocalized presaccadic stimuli as being closer to the target. Our results show that RF shifts do not predict the retinal displacements due to saccades, but instead reflect the overriding perception of target space during eye movements.

Distinct Structure of Cortical Population Activity on Fast and Infraslow Timescales.

Cortical activity is organized 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 0.01-100 Hz frequency range. In mouse medial prefrontal cortex, 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 anticorrelated with the population. Infraslow firing rate changes were largely determined by arousal rather than by local factors, which could explain the timescale dependence of individual neurons' population coupling strength. These observations demonstrate how 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.

Does the Superior Colliculus Control Perceptual Sensitivity or Choice Bias during Attention? Evidence from a Multialternative Decision Framework.

Distinct networks in the forebrain and the midbrain coordinate to control spatial attention. The critical involvement of the superior colliculus (SC)-the central structure in the midbrain network-in visuospatial attention has been shown by four seminal, published studies in monkeys (Macaca mulatta) performing multialternative tasks. However, due to the lack of a mechanistic framework for interpreting behavioral data in such tasks, the nature of the SC's contribution to attention remains unclear. Here we present and validate a novel decision framework for analyzing behavioral data in multialternative attention tasks. We apply this framework to re-examine the behavioral evidence from these published studies. Our model is a multidimensional extension to signal detection theory that distinguishes between two major classes of attentional mechanisms: those that alter the quality of sensory information or "sensitivity," and those that alter the selective gating of sensory information or "choice bias." Model-based simulations and model-based analyses of data from these published studies revealed a converging pattern of results that indicated that choice-bias changes, rather than sensitivity changes, were the primary outcome of SC manipulation. Our results suggest that the SC contributes to attentional performance predominantly by generating a spatial choice bias for stimuli at a selected location, and that this bias operates downstream of forebrain mechanisms that enhance sensitivity. The findings lead to a testable mechanistic framework of how the midbrain and forebrain networks interact to control spatial attention. SIGNIFICANCE STATEMENT: Attention involves the selection of the most relevant information for differential sensory processing and decision making. While the mechanisms by which attention alters sensory encoding (sensitivity control) are well studied, the mechanisms by which attention alters decisional weighting of sensory evidence (choice-bias control) are poorly understood. Here, we introduce a model of multialternative decision making that distinguishes bias from sensitivity effects in attention tasks. With our model, we simulate experimental data from four seminal studies that microstimulated or inactivated a key attention-related midbrain structure, the superior colliculus (SC). We demonstrate that the experimental effects of SC manipulation are entirely consistent with the SC controlling attention by changing choice bias, thereby shedding new light on how the brain mediates attention.

Distinguishing bias from sensitivity effects in multialternative detection tasks.

Studies investigating the neural bases of cognitive phenomena increasingly employ multialternative detection tasks that seek to measure the ability to detect a target stimulus or changes in some target feature (e.g., orientation or direction of motion) that could occur at one of many locations. In such tasks, it is essential to distinguish the behavioral and neural correlates of enhanced perceptual sensitivity from those of increased bias for a particular location or choice (choice bias). However, making such a distinction is not possible with established approaches. We present a new signal detection model that decouples the behavioral effects of choice bias from those of perceptual sensitivity in multialternative (change) detection tasks. By formulating the perceptual decision in a multidimensional decision space, our model quantifies the respective contributions of bias and sensitivity to multialternative behavioral choices. With a combination of analytical and numerical approaches, we demonstrate an optimal, one-to-one mapping between model parameters and choice probabilities even for tasks involving arbitrarily large numbers of alternatives. We validated the model with published data from two ternary choice experiments: a target-detection experiment and a length-discrimination experiment. The results of this validation provided novel insights into perceptual processes (sensory noise and competitive interactions) that can accurately and parsimoniously account for observers' behavior in each task. The model will find important application in identifying and interpreting the effects of behavioral manipulations (e.g., cueing attention) or neural perturbations (e.g., stimulation or inactivation) in a variety of multialternative tasks of perception, attention, and decision-making.

The spiking output of the mouse olfactory bulb encodes large-scale temporal features of natural odor environments.

Spatiotemporal dynamics of natural odor environment have informative features for animals navigating to an odor source. Population activity in the olfactory bulb (OB) has been shown to follow plume dynamics to a moderate degree (Lewis et al., 2021), but it is unknown whether the ability to follow plume dynamics is driven by individual cells or whether it emerges at the population level. Previous research has explored the responses of individual OB cells to isolated features of plumes, but it is difficult to adequately sample these features as it is still undetermined which features navigating mice employ during olfactory guided search. Here we released odor from an upwind odor source and simultaneously recorded both odor concentration dynamics and cellular response dynamics in awake, head-fixed mice. We found that longer timescale features of odor concentration dynamics were encoded at both the cellular and population level. At the cellular level, plume onset was encoded across all trials and plume offset was encoded for high concentration odors, but not low concentration odors. Although cellular level tracking of plume dynamics was observed to be weak, we found that at the population level, OB activity distinguished whiffs and blanks (accurately detected odor presence versus absence) throughout the duration of a plume. Even ~20 OB cells were enough to accurately encode these features. Our findings indicate that the full range of odor concentration dynamics and high frequency fluctuations are not encoded by OB spiking activity. Instead, relatively lower-frequency dynamics of plumes, such as plume onset, plume offset, whiffs, and blanks, are represented in the OB.

Interactive data exploration websites for large-scale electrophysiology.

Methodological advances in neuroscience have enabled the collection of massive datasets which demand innovative approaches for scientific communication. Existing platforms for data storage lack intuitive tools for data exploration, limiting our ability to interact effectively with these brain-wide datasets. We introduce two public websites: (Data and Atlas) developed for the International Brain Laboratory which provide access to millions of behavioral trials and hundreds of thousands of individual neurons. These interfaces allow users to discover both the raw and processed brain-wide data released by the IBL at the scale of the whole brain, individual sessions, trials, and neurons. By hosting these data interfaces as websites they are available cross-platform with no installation. By releasing each site's code as a modular open-source framework, other researchers can easily develop their own web interfaces and explore their own data. As neuroscience datasets continue to expand, customizable web interfaces offer a glimpse into a future of streamlined data exploration and act as blueprints for future tools.

Ultra-high density electrodes improve detection, yield, and cell type identification in neuronal recordings.

To understand the neural basis of behavior, it is essential to sensitively and accurately measure neural activity at single neuron and single spike resolution. Extracellular electrophysiology delivers this, but it has biases in the neurons it detects and it imperfectly resolves their action potentials. To minimize these limitations, we developed a silicon probe with much smaller and denser recording sites than previous designs, called Neuropixels Ultra (NP Ultra). This device samples neuronal activity at ultra-high spatial density (~10 times higher than previous probes) with low noise levels, while trading off recording span. NP Ultra is effectively an implantable voltage-sensing camera that captures a planar image of a neuron's electrical field. We use a spike sorting algorithm optimized for these probes to demonstrate that the yield of visually-responsive neurons in recordings from mouse visual cortex improves up to ~3-fold. We show that NP Ultra can record from small neuronal structures including axons and dendrites. Recordings across multiple brain regions and four species revealed a subset of extracellular action potentials with unexpectedly small spatial spread and axon-like features. We share a large-scale dataset of these brain-wide recordings in mice as a resource for studies of neuronal biophysics. Finally, using ground-truth identification of three major inhibitory cortical cell types, we found that these cell types were discriminable with approximately 75% success, a significant improvement over lower-resolution recordings. NP Ultra improves spike sorting performance, detection of subcellular compartments, and cell type classification to enable more powerful dissection of neural circuit activity during behavior.

A stable, distributed code for cue value in mouse cortex during reward learning.

The ability to associate reward-predicting stimuli with adaptive behavior is frequently attributed to the prefrontal cortex, but the stimulus-specificity, spatial distribution, and stability of prefrontal cue-reward associations are unresolved. We trained head-fixed mice on an olfactory Pavlovian conditioning task and measured the coding properties of individual neurons across space (prefrontal, olfactory, and motor cortices) and time (multiple days). Neurons encoding cues or licks were most common in the olfactory and motor cortex, respectively. By quantifying the responses of cue-encoding neurons to six cues with varying probabilities of reward, we unexpectedly found value coding in all regions we sampled, with some enrichment in the prefrontal cortex. We further found that prefrontal cue and lick codes were preserved across days. Our results demonstrate that individual prefrontal neurons stably encode components of cue-reward learning within a larger spatial gradient of coding properties.

Pinpoint: trajectory planning for multi-probe electrophysiology and injections in an interactive web-based 3D environment.

Targeting deep brain structures during electrophysiology and injections requires intensive training and expertise. Even with experience, researchers often can't be certain that a probe is placed precisely in a target location and this complexity scales with the number of simultaneous probes used in an experiment. Here, we present Pinpoint, open-source software that allows for interactive exploration of stereotaxic insertion plans. Once an insertion plan is created, Pinpoint allows users to save these online and share them with collaborators. 3D modeling tools allow users to explore their insertions alongside rig and implant hardware and ensure plans are physically possible. Probes in Pinpoint can be linked to electronic micro-manipulators allowing real-time visualization of current brain region targets alongside neural data. In addition, Pinpoint can control manipulators to automate and parallelize the insertion process. Compared to previously available software, Pinpoint's easy access through web browsers, extensive features, and real-time experiment integration enable more efficient and reproducible recordings.

Intrinsic timescales in the visual cortex change with selective attention and reflect spatial connectivity.

Intrinsic timescales characterize dynamics of endogenous fluctuations in neural activity. Variation of intrinsic timescales across the neocortex reflects functional specialization of cortical areas, but less is known about how intrinsic timescales change during cognitive tasks. We measured intrinsic timescales of local spiking activity within columns of area V4 in male monkeys performing spatial attention tasks. The ongoing spiking activity unfolded across at least two distinct timescales, fast and slow. The slow timescale increased when monkeys attended to the receptive fields location and correlated with reaction times. By evaluating predictions of several network models, we found that spatiotemporal correlations in V4 activity were best explained by the model in which multiple timescales arise from recurrent interactions shaped by spatially arranged connectivity, and attentional modulation of timescales results from an increase in the efficacy of recurrent interactions. Our results suggest that multiple timescales may arise from the spatial connectivity in the visual cortex and flexibly change with the cognitive state due to dynamic effective interactions between neurons.

Bypassing spike sorting: Density-based decoding using spike localization from dense multielectrode probes.

Neural decoding and its applications to brain computer interfaces (BCI) are essential for understanding the association between neural activity and behavior. A prerequisite for many decoding approaches is spike sorting, the assignment of action potentials (spikes) to individual neurons. Current spike sorting algorithms, however, can be inaccurate and do not properly model uncertainty of spike assignments, therefore discarding information that could potentially improve decoding performance. Recent advances in high-density probes (e.g., Neuropixels) and computational methods now allow for extracting a rich set of spike features from unsorted data; these features can in turn be used to directly decode behavioral correlates. To this end, we propose a spike sorting-free decoding method that directly models the distribution of extracted spike features using a mixture of Gaussians (MoG) encoding the uncertainty of spike assignments, without aiming to solve the spike clustering problem explicitly. We allow the mixing proportion of the MoG to change over time in response to the behavior and develop variational inference methods to fit the resulting model and to perform decoding. We benchmark our method with an extensive suite of recordings from different animals and probe geometries, demonstrating that our proposed decoder can consistently outperform current methods based on thresholding (i.e. multi-unit activity) and spike sorting. Open source code is available at https://github.com/yzhang511/density_decoding.

DREDge: robust motion correction for high-density extracellular recordings across species.

High-density microelectrode arrays (MEAs) have opened new possibilities for systems neuroscience in human and non-human animals, but brain tissue motion relative to the array poses a challenge for downstream analyses, particularly in human recordings. We introduce DREDge (Decentralized Registration of Electrophysiology Data), a robust algorithm which is well suited for the registration of noisy, nonstationary extracellular electrophysiology recordings. In addition to estimating motion from spikes in the action potential (AP) frequency band, DREDge enables automated tracking of motion at high temporal resolution in the local field potential (LFP) frequency band. In human intraoperative recordings, which often feature fast (period <1s) motion, DREDge correction in the LFP band enabled reliable recovery of evoked potentials, and significantly reduced single-unit spike shape variability and spike sorting error. Applying DREDge to recordings made during deep probe insertions in nonhuman primates demonstrated the possibility of tracking probe motion of centimeters across several brain regions while simultaneously mapping single unit electrophysiological features. DREDge reliably delivered improved motion correction in acute mouse recordings, especially in those made with an recent ultra-high density probe. We also implemented a procedure for applying DREDge to recordings made across tens of days in chronic implantations in mice, reliably yielding stable motion tracking despite changes in neural activity across experimental sessions. Together, these advances enable automated, scalable registration of electrophysiological data across multiple species, probe types, and drift cases, providing a stable foundation for downstream scientific analyses of these rich datasets.

Cortical state dynamics and selective attention define the spatial pattern of correlated variability in neocortex.

Correlated activity fluctuations in the neocortex influence sensory responses and behavior. Neural correlations reflect anatomical connectivity but also change dynamically with cognitive states such as attention. Yet, the network mechanisms defining the population structure of correlations remain unknown. We measured correlations within columns in the visual cortex. We show that the magnitude of correlations, their attentional modulation, and dependence on lateral distance are explained by columnar On-Off dynamics, which are synchronous activity fluctuations reflecting cortical state. We developed a network model in which the On-Off dynamics propagate across nearby columns generating spatial correlations with the extent controlled by attentional inputs. This mechanism, unlike previous proposals, predicts spatially non-uniform changes in correlations during attention. We confirm this prediction in our columnar recordings by showing that in superficial layers the largest changes in correlations occur at intermediate lateral distances. Our results reveal how spatially structured patterns of correlated variability emerge through interactions of cortical state dynamics, anatomical connectivity, and attention.

A scale-dependent measure of system dimensionality.

A fundamental problem in science is uncovering the effective number of degrees of freedom in a complex system: its dimensionality. A system's dimensionality depends on its spatiotemporal scale. Here, we introduce a scale-dependent generalization of a classic enumeration of latent variables, the participation ratio. We demonstrate how the scale-dependent participation ratio identifies the appropriate dimension at local, intermediate, and global scales in several systems such as the Lorenz attractor, hidden Markov models, and switching linear dynamical systems. We show analytically how, at different limiting scales, the scale-dependent participation ratio relates to well-established measures of dimensionality. This measure applied in neural population recordings across multiple brain areas and brain states shows fundamental trends in the dimensionality of neural activity-for example, in behaviorally engaged versus spontaneous states. Our novel method unifies widely used measures of dimensionality and applies broadly to multivariate data across several fields of science.