Brain-wide neural activity underlying memory-guided movement.

Behavior relies on activity in structured neural circuits that are distributed across the brain, but most experiments probe neurons in a single area at a time. Using multiple Neuropixels probes, we recorded from multi-regional loops connected to the anterior lateral motor cortex (ALM), a circuit node mediating memory-guided directional licking. Neurons encoding sensory stimuli, choices, and actions were distributed across the brain. However, choice coding was concentrated in the ALM and subcortical areas receiving input from the ALM in an ALM-dependent manner. Diverse orofacial movements were encoded in the hindbrain; midbrain; and, to a lesser extent, forebrain. Choice signals were first detected in the ALM and the midbrain, followed by the thalamus and other brain areas. At movement initiation, choice-selective activity collapsed across the brain, followed by new activity patterns driving specific actions. Our experiments provide the foundation for neural circuit models of decision-making and movement initiation.

A midbrain-thalamus-cortex circuit reorganizes cortical dynamics to initiate movement.

Motor behaviors are often planned long before execution but only released after specific sensory events. Planning and execution are each associated with distinct patterns of motor cortex activity. Key questions are how these dynamic activity patterns are generated and how they relate to behavior. Here, we investigate the multi-regional neural circuits that link an auditory "Go cue" and the transition from planning to execution of directional licking. Ascending glutamatergic neurons in the midbrain reticular and pedunculopontine nuclei show short latency and phasic changes in spike rate that are selective for the Go cue. This signal is transmitted via the thalamus to the motor cortex, where it triggers a rapid reorganization of motor cortex state from planning-related activity to a motor command, which in turn drives appropriate movement. Our studies show how midbrain can control cortical dynamics via the thalamus for rapid and precise motor behavior.

Sparse Coding in Temporal Association Cortex Improves Complex Sound Discriminability.

The mouse auditory cortex is comprised of several auditory fields spanning the dorsoventral axis of the temporal lobe. The ventral most auditory field is the temporal association cortex (TeA), which remains largely unstudied. Using Neuropixels probes, we simultaneously recorded from primary auditory cortex (AUDp), secondary auditory cortex (AUDv), and TeA, characterizing neuronal responses to pure tones and frequency modulated (FM) sweeps in awake head-restrained female mice. As compared with AUDp and AUDv, single-unit (SU) responses to pure tones in TeA were sparser, delayed, and prolonged. Responses to FMs were also sparser. Population analysis showed that the sparser responses in TeA render it less sensitive to pure tones, yet more sensitive to FMs. When characterizing responses to pure tones under anesthesia, the distinct signature of TeA was changed considerably as compared with that in awake mice, implying that responses in TeA are strongly modulated by non-feedforward connections. Together, these findings provide a basic electrophysiological description of TeA as an integral part of sound processing along the cortical hierarchy.SIGNIFICANCE STATEMENT This is the first comprehensive characterization of the auditory responses in the awake mouse auditory temporal association cortex (TeA). The study provides the foundations for further investigation of TeA and its involvement in auditory learning, plasticity, auditory driven behaviors etc. The study was conducted using state of the art data collection tools, allowing for simultaneous recording from multiple cortical regions and numerous neurons.

Population burst propagation across interacting areas of the brain.

For many perceptual and behavioral tasks, a prominent feature of neural spike trains involves high firing rates across relatively short intervals of time. We call these events "population bursts." Because during a population burst information is, presumably, transmitted from one part of the brain to another, burst timing should reveal activity related to the flow of information across neural circuits. We developed a statistical method (based on a point process model) of determining, accurately, the time of the maximum (peak) population firing rate on a trial-by-trial basis and used it to characterize burst propagation across areas. We then examined the tendency of peak firing rates in distinct brain areas to shift earlier or later in time, together, across repeated trials, and found this trial-to-trial coupling of peak times to be a sensitive indicator of interaction across populations. In the data we examined, from the Allen Brain Observatory, we found many very strong correlations (95% confidence intervals above 0.75) in cases where standard methods were unable to demonstrate cross-area correlation. The statistical model introduced cross-area covariation only through population-level trial-dependent time shifts and gain constants (values of which were learned from the data), yet it provided very good fits to data histograms, including histograms of spike count correlations within and across visual areas. Our results demonstrate the utility of carefully assessing timing and propagation, across brain regions, of transient bursts in neural population activity, based on multiple spike train recordings.NEW & NOTEWORTHY We developed a novel statistical method for identifying coordinated propagation of activity across populations of spiking neurons, with high temporal accuracy. Using simultaneous recordings from three visual areas we document precise timing relationships on a trial-by-trial basis, and we show how previously existing techniques can fail to discover coordinated activity in cases where the new approach finds very strong cross-area correlation.

High-density extracellular probes reveal dendritic backpropagation and facilitate neuron classification.

Different neuron types serve distinct roles in neural processing. Extracellular electrical recordings are extensively used to study brain function but are typically blind to cell identity. Morphoelectrical properties of neurons measured on spatially dense electrode arrays have the potential to distinguish neuron types. We used high-density silicon probes to record from cortical and subcortical regions of the mouse brain. Extracellular waveforms of each neuron were detected across many channels and showed distinct spatiotemporal profiles among brain regions. Classification of neurons by brain region was improved with multichannel compared with single-channel waveforms. In visual cortex, unsupervised clustering identified the canonical regular-spiking (RS) and fast-spiking (FS) classes but also indicated a subclass of RS units with unidirectional backpropagating action potentials (BAPs). Moreover, BAPs were observed in many hippocampal RS cells. Overall, waveform analysis of spikes from high-density probes aids neuron identification and can reveal dendritic backpropagation. NEW & NOTEWORTHY It is challenging to identify neuron types with extracellular electrophysiology in vivo. We show that spatiotemporal action potentials measured on high-density electrode arrays can capture cell type-specific morphoelectrical properties, allowing classification of neurons across brain structures and within the cortex. Moreover, backpropagating action potentials are reliably detected in vivo from subpopulations of cortical and hippocampal neurons. Together, these results enhance the utility of dense extracellular electrophysiology for cell-type interrogation of brain network function.

Acute Neuropixels Recordings in the Marmoset Monkey.

High-density linear probes, such as Neuropixels, provide an unprecedented opportunity to understand how neural populations within specific laminar compartments contribute to behavior. Marmoset monkeys, unlike macaque monkeys, have a lissencephalic (smooth) cortex that enables recording perpendicular to the cortical surface, thus making them an ideal animal model for studying laminar computations. Here we present a method for acute Neuropixels recordings in the common marmoset (Callithrix jacchus). The approach replaces the native dura with an artificial silicon-based dura that grants visual access to the cortical surface, which is helpful in avoiding blood vessels, ensures perpendicular penetrations, and could be used in conjunction with optical imaging or optogenetic techniques. The chamber housing the artificial dura is simple to maintain with minimal risk of infection and could be combined with semichronic microdrives and wireless recording hardware. This technique enables repeated acute penetrations over a period of several months. With occasional removal of tissue growth on the pial surface, recordings can be performed for a year or more. The approach is fully compatible with Neuropixels probes, enabling the recording of hundreds of single neurons distributed throughout the cortical column.

Multi-day neuron tracking in high-density electrophysiology recordings using earth mover's distance.

Accurate tracking of the same neurons across multiple days is crucial for studying changes in neuronal activity during learning and adaptation. Advances in high-density extracellular electrophysiology recording probes, such as Neuropixels, provide a promising avenue to accomplish this goal. Identifying the same neurons in multiple recordings is, however, complicated by non-rigid movement of the tissue relative to the recording sites (drift) and loss of signal from some neurons. Here, we propose a neuron tracking method that can identify the same cells independent of firing statistics, that are used by most existing methods. Our method is based on between-day non-rigid alignment of spike-sorted clusters. We verified the same cell identity in mice using measured visual receptive fields. This method succeeds on datasets separated from 1 to 47 days, with an 84% average recovery rate.

Cortical cellular encoding of thermotactile integration.

Recent evidence suggests that primary sensory cortical regions play a role in the integration of information from multiple sensory modalities. How primary cortical neurons integrate different sources of sensory information is unclear, partly because non-primary sensory input to a cortical sensory region is often weak or modulatory. To address this question, we take advantage of the robust representation of thermal (cooling) and tactile stimuli in mouse forelimb primary somatosensory cortex (fS1). Using a thermotactile detection task, we show that the perception of threshold-level cool or tactile information is enhanced when they are presented simultaneously, compared with presentation alone. To investigate the cortical cellular correlates of thermotactile integration, we performed in vivo extracellular recordings from fS1 in awake resting and anesthetized mice during unimodal and bimodal stimulation of the forepaw. Unimodal stimulation evoked thermal- or tactile- specific excitatory and inhibitory responses of fS1 neurons. The most prominent features of combined thermotactile stimulation are the recruitment of unimodally silent fS1 neurons, non-linear integration features, and response dynamics that favor longer response durations with additional spikes. Together, we identify quantitative and qualitative changes in cortical encoding that may underlie the improvement in perception of thermotactile surfaces during haptic exploration.

Recording the brain in vivo: emerging technologies for the exploration of mental health conditions.

Standfirst Mounting interest in mental health conditions over the last two decades has been coupled with the increasing sophistication of techniques to study the brain in vivo. [Formula: see text].

A deep-learning strategy to identify cell types across species from high-density extracellular recordings.

High-density probes allow electrophysiological recordings from many neurons simultaneously across entire brain circuits but don't reveal cell type. Here, we develop a strategy to identify cell types from extracellular recordings in awake animals, revealing the computational roles of neurons with distinct functional, molecular, and anatomical properties. We combine optogenetic activation and pharmacology using the cerebellum as a testbed to generate a curated ground-truth library of electrophysiological properties for Purkinje cells, molecular layer interneurons, Golgi cells, and mossy fibers. We train a semi-supervised deep-learning classifier that predicts cell types with greater than 95% accuracy based on waveform, discharge statistics, and layer of the recorded neuron. The classifier's predictions agree with expert classification on recordings using different probes, in different laboratories, from functionally distinct cerebellar regions, and across animal species. Our classifier extends the power of modern dynamical systems analyses by revealing the unique contributions of simultaneously-recorded cell types during behavior.

Preconfigured architecture of the developing mouse brain.

In the adult brain, structural and functional parameters, such as synaptic sizes and neuronal firing rates, follow right-skewed and heavy-tailed distributions. While this organization is thought to have significant implications, its development is still largely unknown. Here, we address this knowledge gap by investigating a large-scale dataset recorded from the prefrontal cortex and the olfactory bulb of mice aged 4-60 postnatal days. We show that firing rates and spike train interactions have a largely stable distribution shape throughout the first 60 postnatal days and that the prefrontal cortex displays a functional small-world architecture. Moreover, early brain activity exhibits an oligarchical organization, where high-firing neurons have hub-like properties. In a neural network model, we show that analogously right-skewed and heavy-tailed synaptic parameters are instrumental to consistently recapitulate the experimental data. Thus, functional and structural parameters in the developing brain are already extremely distributed, suggesting that this organization is preconfigured and not experience dependent.

SHIELD: Skull-shaped hemispheric implants enabling large-scale electrophysiology datasets in the mouse brain.

To understand the neural basis of behavior, it is essential to measure spiking dynamics across many interacting brain regions. Although new technologies, such as Neuropixels probes, facilitate multi-regional recordings, significant surgical and procedural hurdles remain for these experiments to achieve their full potential. Here, we describe skull-shaped hemispheric implants enabling large-scale electrophysiology datasets (SHIELD). These 3D-printed skull-replacement implants feature customizable insertion holes, allowing dozens of cortical and subcortical structures to be recorded in a single mouse using repeated multi-probe insertions over many days. We demonstrate the procedure's high success rate, biocompatibility, lack of adverse effects on behavior, and compatibility with imaging and optogenetics. To showcase SHIELD's scientific utility, we use multi-probe recordings to reveal novel insights into how alpha rhythms organize spiking activity across visual and sensorimotor networks. Overall, this method enables powerful, large-scale electrophysiological experiments for the study of distributed neural computation.

Embryonically active piriform cortex neurons promote intracortical recurrent connectivity during development.

Neuronal activity plays a critical role in the maturation of circuits that propagate sensory information into the brain. How widely does early activity regulate circuit maturation across the developing brain? Here, we used targeted recombination in active populations (TRAP) to perform a brain-wide survey for prenatally active neurons in mice and identified the piriform cortex as an abundantly TRAPed region. Whole-cell recordings in neonatal slices revealed preferential interconnectivity within embryonically TRAPed piriform neurons and their enhanced synaptic connectivity with other piriform neurons. In vivo Neuropixels recordings in neonates demonstrated that embryonically TRAPed piriform neurons exhibit broad functional connectivity within piriform and lead spontaneous synchronized population activity during a transient neonatal period, when recurrent connectivity is strengthening. Selectively activating or silencing these neurons in neonates enhanced or suppressed recurrent synaptic strength, respectively. Thus, embryonically TRAPed piriform neurons represent an interconnected hub-like population whose activity promotes recurrent connectivity in early development.

Long-term near-continuous recording with Neuropixels probes in healthy and epileptic rats.

Neuropixels probes have become a crucial tool for high-density electrophysiological recordings. Although most research involving these probes is in acute preparations, some scientific inquiries require long-term recordings in freely moving animals. Recent reports have presented prosthesis designs for chronic recordings, but some of them do not allow for probe recovery, which is desirable given their cost. Others appear to be fragile, as these articles describe numerous broken implants.Objective.This fragility presents a challenge for recordings in rats, particularly in epilepsy models where strong mechanical stress impinges upon the prosthesis. To overcome these limitations, we sought to develop a new prosthesis for long-term electrophysiological recordings in healthy and epileptic rats.Approach.We present a new prosthesis specifically designed to protect the probes from strong shocks and enable the safe retrieval of probes after experiments.Main results.This prosthesis was successfully used to record from healthy and epileptic rats for up to three weeks almost continuously. Overall, 10 out of 11 probes could be successfully retrieved with a retrieval and reuse success rate of 91%.Significance.Our design and protocol significantly improved previously described probe recycling performances and prove usage on epileptic rats.

3D printed guide tube system for acute Neuropixels probe recordings in non-human primates.

Objective.Neuropixels (NP) probes are a significant advance in electrophysiological recording technology that enable monitoring of hundreds of neurons in the brain simultaneously at different depths. Application of this technology has been predominately in rodents, however widespread use in non-human primates (NHPs) such as rhesus macaques has been limited. In this study we sought to overcome two overarching challenges that impede acute NP implantation in NHPs: (1) traditional microdrive systems that mount to cephalic chambers are commonly used to access cortical areas for microelectrode recordings but are not designed to accommodate NP probes, and (2) NHPs have thick dura mater and tissue growth within the cephalic chambers which poses a challenge for insertion of the extremely fragile NP probe.Approach.In this study we present a novel NP guide tube system that can be adapted to commercial microdrive systems and demonstrate an implant method using the NP guide tube system. This system was developed using a combination of CAD design, 3D printing, and small part machining. Software programs, 3D Slicer and SolidWorks were used to target cortical areas, approximate recording depths and locations, and for in-silico implant testing.Main results.We performedin vivotesting to validate our methodology, successfully implanting, explanting, and reimplanting NP probes. We collected stable neurophysiological recordings in the premotor cortex of a rhesus macaque at rest and during performance of a reaching task.Significance.In this study we demonstrate a robust Neuropixels implant system that allows multiple penetrations with the same NP probe and share design files that will facilitate the adoption of this powerful recording technology for NHP studies.

Influence of claustrum on cortex varies by area, layer, and cell type.

The claustrum is a small subcortical structure with widespread connections to disparate regions of the cortex. However, the impact of the claustrum on cortical activity is not fully understood, particularly beyond frontal areas. Here, using optogenetics and multi-regional Neuropixels recordings from over 15,000 cortical neurons in awake mice, we demonstrate that the effect of claustrum input to the cortex differs depending on brain area, layer, and cell type. Brief claustrum stimulation, producing approximately 1 spike per claustrum neuron, affects many fast spiking (FS; putative inhibitory) but relatively fewer regular-spiking (RS; putative excitatory) cortical neurons and leads to a modest decrease in population activity in frontal cortical areas. Prolonged claustrum stimulation affects many more cortical neurons and can increase or decrease spiking activity. More excitation occurs in posterior regions and superficial layers, while inhibition predominates in frontal regions and deeper layers. These findings suggest that claustro-cortical circuits are organized into functional modules.

A scalable implementation of the recursive least-squares algorithm for training spiking neural networks.

Training spiking recurrent neural networks on neuronal recordings or behavioral tasks has become a popular way to study computations performed by the nervous system. As the size and complexity of neural recordings increase, there is a need for efficient algorithms that can train models in a short period of time using minimal resources. We present optimized CPU and GPU implementations of the recursive least-squares algorithm in spiking neural networks. The GPU implementation can train networks of one million neurons, with 100 million plastic synapses and a billion static synapses, about 1,000 times faster than an unoptimized reference CPU implementation. We demonstrate the code's utility by training a network, in less than an hour, to reproduce the activity of > 66, 000 recorded neurons of a mouse performing a decision-making task. The fast implementation enables a more interactive in-silico study of the dynamics and connectivity underlying multi-area computations. It also admits the possibility to train models as in-vivo experiments are being conducted, thus closing the loop between modeling and experiments.

Play and tickling responses map to the lateral columns of the rat periaqueductal gray.

The persistence of play after decortication points to a subcortical mechanism of play control. We found that global blockade of the rat periaqueductal gray with either muscimol or lidocaine interfered with ticklishness and play. We recorded vocalizations and neural activity from the periaqueductal gray of young, playful rats during interspecific touch, play, and tickling. Rats vocalized weakly to touch and more strongly to play and tickling. Periaqueductal gray units showed diverse but strong modulation to tickling and play. Hierarchical clustering based on neuronal responses to play and tickling revealed functional clusters mapping to different periaqueductal gray columns. Specifically, we observed play-neutral/tickling-inhibited and tickling/play-neutral units in dorsolateral and dorsomedial periaqueductal gray columns. In contrast, strongly play/tickling-excited units mapped to the lateral columns and were suppressed by anxiogenic conditions. Optogenetic inactivation of lateral periaqueductal columns disrupted ticklishness and play. We conclude that the lateral periaqueductal gray columns are decisive for play and laughter.

Sensory cortical ensembles exhibit differential coupling to ripples in distinct hippocampal subregions.

Cortical neurons activated during recent experiences often reactivate with dorsal hippocampal CA1 ripples during subsequent rest. Less is known about cortical interactions with intermediate hippocampal CA1, whose connectivity, functions, and ripple events differ from dorsal CA1. We identified three clusters of putative excitatory neurons in mouse visual cortex that are preferentially excited together with either dorsal or intermediate CA1 ripples or suppressed before both ripples. Neurons in each cluster were evenly distributed across primary and higher visual cortices and co-active even in the absence of ripples. These ensembles exhibited similar visual responses but different coupling to thalamus and pupil-indexed arousal. We observed a consistent activity sequence preceding and predicting ripples: (1) suppression of ripple-suppressed cortical neurons, (2) thalamic silence, and (3) activation of intermediate CA1-ripple-activated cortical neurons. We propose that coordinated dynamics of these ensembles relay visual experiences to distinct hippocampal subregions for incorporation into different cognitive maps.

A survey of neurophysiological differentiation across mouse visual brain areas and timescales.

Neurophysiological differentiation (ND), a measure of the number of distinct activity states that a neural population visits over a time interval, has been used as a correlate of meaningfulness or subjective perception of visual stimuli. ND has largely been studied in non-invasive human whole-brain recordings where spatial resolution is limited. However, it is likely that perception is supported by discrete neuronal populations rather than the whole brain. Therefore, here we use Neuropixels recordings from the mouse brain to characterize the ND metric across a wide range of temporal scales, within neural populations recorded at single-cell resolution in localized regions. Using the spiking activity of thousands of simultaneously recorded neurons spanning 6 visual cortical areas and the visual thalamus, we show that the ND of stimulus-evoked activity of the entire visual cortex is higher for naturalistic stimuli relative to artificial ones. This finding holds in most individual areas throughout the visual hierarchy. Moreover, for animals performing an image change detection task, ND of the entire visual cortex (though not individual areas) is higher for successful detection compared to failed trials, consistent with the assumed perception of the stimulus. Together, these results suggest that ND computed on cellular-level neural recordings is a useful tool highlighting cell populations that may be involved in subjective perception.