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Neocortical neurons can increasingly be divided into well-defined classes, but their activity patterns during quantified behavior remain to be fully determined. Here, we obtained membrane potential recordings from various classes of excitatory and inhibitory neurons located across different cortical depths in the primary whisker somatosensory barrel cortex of awake head-restrained mice during quiet wakefulness, free whisking and active touch. Excitatory neurons, especially those located superficially, were hyperpolarized with low action potential firing rates relative to inhibitory neurons. Parvalbumin-expressing inhibitory neurons on average fired at the highest rates, responding strongly and rapidly to whisker touch. Vasoactive intestinal peptide-expressing inhibitory neurons were excited during whisking, but responded to active touch only after a delay. Somatostatin-expressing inhibitory neurons had the smallest membrane potential fluctuations and exhibited hyperpolarising responses at whisking onset for superficial, but not deep, neurons. Interestingly, rapid repetitive whisker touch evoked excitatory responses in somatostatin-expressing inhibitory neurons, but not when the intercontact interval was long. Our analyses suggest that distinct genetically-defined classes of neurons at different subpial depths have differential activity patterns depending upon behavioral state providing an important basis for constraining future computational models of neocortical function.

/SummaryAs the tools to simultaneously record electrophysiological signals from large numbers of neurons within and across brain regions become increasingly available, this opens up for the first time the possibility of establishing the details of causal relationships between monosynaptically connected neurons and the patterns of neural activation that underlie perception and behavior. Although recorded activity across synaptically connected neurons has served as the cornerstone for much of what we know about synaptic transmission and plasticity, this has largely been relegated to ex-vivo preparations that enable precise targeting under relatively well-controlled conditions. Analogous studies in-vivo, where image-guided targeting is often not yet possible, rely on indirect, data-driven measures, and as a result such studies have been sparse and the dependence upon important experimental parameters has not been well studied. Here, using in-vivo extracellular single unit recordings in the topographically aligned rodent thalamocortical pathway, we sought to establish a general experimental and computational framework for inferring synaptic connectivity. Specifically, attacking this problem within a statistical signal-detection framework utilizing experimentally recorded data in the ventral-posterior medial (VPm) region of the thalamus and the homologous region in layer 4 of primary somatosensory cortex (S1) revealed a trade-off between network activity levels needed for the data-driven inference and synchronization of nearby neurons within the population that result in masking of synaptic relationships. Taken together, we provide a framework for establishing connectivity in multi-site, multi-electrode recordings based on statistical inference, setting the stage for large-scale assessment of synaptic connectivity within and across brain structures.New & NoteworthyDespite the fact that all brain function relies on the long-range transfer of information across different regions, the tools enabling us to measure connectivity across brain structures are lacking. Here, we provide a statistical framework for identifying and assessing potential monosynaptic connectivity across neuronal circuits from population spiking activity that generalizes to large-scale recording technologies that will help us to better understand the signaling within networks that underlies perception and behavior.

During wakefulness, cortical networks exhibit diverse states of activity arising from fluctuations in modulatory inputs, any of which may shape the recorded neural activity across a range of scales, from local field potential (LFP) to the spiking of individual neurons. Multiple studies have uncovered state-dependent changes in single neuron activity specific to cell type. These changes are heterogeneous across cell types, suggesting that there is a link between changes in states of wakefulness and the dynamic patterns of spiking activity across the population, yet this is currently not well understood. Here, we use an unsupervised statistical modeling framework to link fluctuations in the state of the LFP to the dynamics of spiking activity of populations of neurons in the whisker area of primary somatosensory cortex of awake male mice. By identifying latent states from population spiking activity, we show that these spiking states are more informative of states of the LFP than a direct readout of the spiking activity of single neurons or of the population. Further, we find that the latent spiking states predict trial-by-trial variability in sensory responses as well as one aspect of behavior, whisking activity. Taken together, our approach captures the latent variables that underlie the dynamics of the spiking activity of cortical neuronal populations, providing a framework to relate cortical activity across different scales.Significance statementDuring wakefulness, variability in cortical activity can arise from shifts in arousal or behavior. We analyze the dynamics of two measures of cortical activity, the spiking activity of local populations of neurons and the local field potential (LFP), typically used to track overall levels of arousal. We show that the latent states that capture variability in population spiking activity are highly predictive of LFP state changes, even more so than spiking activity directly. By linking latent states of spiking activity to changes in the LFP, our novel approach bridges fine-scale microcircuits ensemble activity and the meso-scale LFP. Our latent-state framework opens new avenues for extracting information from the LFP as well as identification of subject-level sources of variability.

Lateralization is a hallmark of somatosensory processing in the mammalian brain. However, in addition to their contralateral representation, unilateral tactile stimuli also modulate neuronal activity in somatosensory cortices of the ipsilateral hemisphere. The cellular organization and functional role of these ipsilateral stimulus responses in awake somatosensory cortices, especially regarding stimulus coding, are unknown. Here, we targeted silicon probe recordings to the vibrissa region of primary (S1) and secondary (S2) somatosensory cortex of awake head-fixed mice of either sex while delivering ipsilateral and contralateral whisker stimuli. Ipsilateral stimuli drove larger and more reliable responses in S2 than in S1, and activated a larger fraction of stimulus-responsive neurons. Ipsilateral stimulus-responsive neurons were rare in layer 4 of S1, but were located in equal proportion across all layers in S2. Linear classifier analyses further revealed that decoding of the ipsilateral stimulus was more accurate in S2 than S1, while S1 decoded contralateral stimuli most accurately. These results reveal substantial encoding of ipsilateral stimuli in S1 and especially S2, consistent with the hypothesis that higher cortical areas may integrate tactile inputs across larger portions of space, spanning both sides of the body.S1ignificance StatementTactile information obtained by one side of the body is represented in the activity of neurons of the opposite brain hemisphere. However unilateral tactile stimulation also modulates neuronal activity in the other, or ipsilateral, brain hemisphere. This ipsilateral activity may play an important role in the representation and processing of tactile information, in particular when the sense of touch involves both sides of the body. Our work in the whisker system of awake mice reveals that neocortical ipsilateral activity, in particular that of deep layer excitatory neurons of secondary somatosensory cortex (S2), contains information about the presence and the velocity of unilateral tactile stimuli, which supports a key role for S2 in integrating tactile information across both body sides.

Cortical responses to sensory inputs vary across repeated presentations of identical stimuli, but how this trial-to-trial variability impacts detection of sensory inputs is not fully understood. Using multi-channel local field potential (LFP) recordings in primary somatosensory cortex (S1) of the awake mouse, we optimized a data-driven cortical state classifier to predict single-trial sensory-evoked responses, based on features of the spontaneous, ongoing LFP recorded across cortical layers. Our findings show that, by utilizing an ongoing prediction of the sensory response generated by this state classifier, an ideal observer improves overall detection accuracy and generates robust detection of sensory inputs across various states of ongoing cortical activity in the awake brain, which could have implications for variability in the performance of detection tasks across brain states., Author summary Establishing the link between neural activity and behavior is a central goal of neuroscience. One context in which to examine this link is in a sensory detection task, in which an animal is trained to report the presence of a barely perceptible sensory stimulus. In such tasks, both sensory responses in the brain and behavioral responses are highly variable. A simple hypothesis, originating in signal detection theory, is that perceived inputs generate neural activity that cross some threshold for detection. According to this hypothesis, sensory response variability would predict behavioral variability, but previous studies have not born out this prediction. Further complicating the picture, sensory response variability is partially dependent on the ongoing state of cortical activity, and we wondered whether this could resolve the mismatch between response variability and behavioral variability. Here, we use a computational approach to study an adaptive observer that utilizes an ongoing prediction of sensory responsiveness to detect sensory inputs. This observer has higher overall accuracy than the standard ideal observer. Moreover, because of the adaptation, the observer breaks the direct link between neural and behavioral variability, which could resolve discrepancies arising in past studies. We suggest new experiments to test our theory.

Excitatory projection neurons of the neocortex are thought to play important roles in perceptual and cognitive functions of the brain by directly connecting diverse cortical and subcortical areas. However, many aspects of the anatomical organization of these inter-areal connections are unknown. Here, we studied long-range axonal projections of excitatory layer 2/3 neurons with cell bodies located in mouse primary somatosensory barrel cortex (wS1). As a population, these neurons densely projected to secondary whisker somatosensory cortex (wS2) and primary/secondary whisker motor cortex (wM1/2), with additional axon in the dysgranular zone surrounding the barrel field, perirhinal temporal association cortex and striatum. In three-dimensional reconstructions of 6 individual wS2-projecting neurons and 9 individual wM1/2-projecting neurons, we found that both classes of neurons had extensive local axon in layers 2/3 and 5 of wS1. Neurons projecting to wS2 did not send axon to wM1/2, whereas a small subset of wM1/2-projecting neurons had relatively weak projections to wS2. A small fraction of projection neurons solely targeted wS2 or wM1/2. However, axon collaterals from wS2-projecting and wM1/2-projecting neurons were typically also found in subsets of various additional areas, including the dysgranular zone, perirhinal temporal association cortex and striatum. Our data suggest extensive diversity in the axonal targets selected by individual nearby cortical long-range projection neurons with somata located in layer 2/3 of wS1.

Summary Intracellular recordings of membrane potential in vitro have defined fundamental properties of synaptic communication. Much less is known about the properties of synaptic connectivity and synaptic transmission in vivo. Here, we combined single-cell optogenetics with whole-cell recordings to investigate glutamatergic synaptic transmission in vivo from single identified excitatory neurons onto two genetically defined subtypes of inhibitory GABAergic neurons in layer 2/3 mouse barrel cortex. We found that parvalbumin-expressing (PV) GABAergic neurons received unitary glutamatergic synaptic input with higher probability than somatostatin-expressing (Sst) GABAergic neurons. Unitary excitatory postsynaptic potentials onto PV neurons were also faster and more reliable than inputs onto Sst neurons. Excitatory synapses targeting Sst neurons displayed strong short-term facilitation, while those targeting PV neurons showed little short-term dynamics. Our results largely agree with in vitro measurements. We therefore demonstrate the technical feasibility of assessing functional cell-type-specific synaptic connectivity in vivo, allowing future investigations into context-dependent modulation of synaptic transmission., Highlights • Single-cell optogenetics for precise stimulation of action potentials in vivo • In vivo whole-cell recordings from genetically defined postsynaptic GABAergic neurons • Parvalbumin-expressing neurons receive strong, fast, and reliable excitatory input • Somatostatin-expressing neurons receive longer-lasting, facilitating excitatory input, Pala and Petersen use single-cell optogenetics and two-photon targeted whole-cell recordings to measure synaptic connectivity and synaptic transmission in vivo from excitatory neurons onto parvalbumin-expressing and somatostatin-expressing GABAergic neurons in layer 2/3 of mouse barrel cortex.

In vivo studies of neurophysiology using the whole cell patch-clamp technique enable exquisite access to both intracellular dynamics and cytosol of cells in the living brain but are underrepresented in deep subcortical nuclei because of fouling of the sensitive electrode tip. We have developed an autonomous method to navigate electrodes around obstacles such as blood vessels after identifying them as a source of contamination during regional pipette localization (RPL) in vivo. In mice, robotic navigation prevented fouling of the electrode tip, increasing RPL success probability 3 mm below the pial surface to 82% (