Neuronal Circuits in Barrel Cortex for Whisker Sensory Perception.

The array of whiskers on the snout provides rodents with tactile sensory information relating to the size, shape and texture of objects in their immediate environment. Rodents can use their whiskers to detect stimuli, distinguish textures, locate objects and navigate. Important aspects of whisker sensation are thought to result from neuronal computations in the whisker somatosensory cortex (wS1). Each whisker is individually represented in the somatotopic map of wS1 by an anatomical unit named a 'barrel' (hence also called barrel cortex). This allows precise investigation of sensory processing in the context of a well-defined map. Here, we first review the signaling pathways from the whiskers to wS1, and then discuss current understanding of the various types of excitatory and inhibitory neurons present within wS1. Different classes of cells can be defined according to anatomical, electrophysiological and molecular features. The synaptic connectivity of neurons within local wS1 microcircuits, as well as their long-range interactions and the impact of neuromodulators, are beginning to be understood. Recent technological progress has allowed cell-type-specific connectivity to be related to cell-type-specific activity during whisker-related behaviors. An important goal for future research is to obtain a causal and mechanistic understanding of how selected aspects of tactile sensory information are processed by specific types of neurons in the synaptically connected neuronal networks of wS1 and signaled to downstream brain areas, thus contributing to sensory-guided decision-making.

Learning-related congruent and incongruent changes of excitation and inhibition in distinct cortical areas.

Excitatory and inhibitory neurons in diverse cortical regions are likely to contribute differentially to the transformation of sensory information into goal-directed motor plans. Here, we investigate the relative changes across mouse sensorimotor cortex in the activity of putative excitatory and inhibitory neurons-categorized as regular spiking (RS) or fast spiking (FS) according to their action potential (AP) waveform-comparing before and after learning of a whisker detection task with delayed licking as perceptual report. Surprisingly, we found that the whisker-evoked activity of RS versus FS neurons changed in opposite directions after learning in primary and secondary whisker motor cortices, while it changed similarly in primary and secondary orofacial motor cortices. Our results suggest that changes in the balance of excitation and inhibition in local circuits concurrent with changes in the long-range synaptic inputs in distinct cortical regions might contribute to performance of delayed sensory-to-motor transformation.

Sensorimotor processing in the rodent barrel cortex.

Tactile sensory information from facial whiskers provides nocturnal tunnel-dwelling rodents, including mice and rats, with important spatial and textural information about their immediate surroundings. Whiskers are moved back and forth to scan the environment (whisking), and touch signals from each whisker evoke sparse patterns of neuronal activity in whisker-related primary somatosensory cortex (wS1; barrel cortex). Whisking is accompanied by desynchronized brain states and cell-type-specific changes in spontaneous and evoked neuronal activity. Tactile information, including object texture and location, appears to be computed in wS1 through integration of motor and sensory signals. wS1 also directly controls whisker movements and contributes to learned, whisker-dependent, goal-directed behaviours. The cell-type-specific neuronal circuitry in wS1 that contributes to whisker sensory perception is beginning to be defined.

Cortical control of whisker movement.

Facial muscles drive whisker movements, which are important for active tactile sensory perception in mice and rats. These whisker muscles are innervated by cholinergic motor neurons located in the lateral facial nucleus. The whisker motor neurons receive synaptic inputs from premotor neurons, which are located within the brain stem, the midbrain, and the neocortex. Complex, distributed neural circuits therefore regulate whisker movement during behavior. This review focuses specifically on cortical whisker motor control. The whisker primary motor cortex (M1) strongly innervates brain stem reticular nuclei containing whisker premotor neurons, which might form a central pattern generator for rhythmic whisker protraction. In a parallel analogous pathway, the whisker primary somatosensory cortex (S1) strongly projects to the brain stem spinal trigeminal interpolaris nucleus, which contains whisker premotor neurons innervating muscles for whisker retraction. These anatomical pathways may play important functional roles, since stimulation of M1 drives exploratory rhythmic whisking, whereas stimulation of S1 drives whisker retraction.

3D Ultrastructure of Synaptic Inputs to Distinct GABAergic Neurons in the Mouse Primary Visual Cortex.

Synapses are the fundamental elements of the brain's complicated neural networks. Although the ultrastructure of synapses has been extensively studied, the difference in how synaptic inputs are organized onto distinct neuronal types is not yet fully understood. Here, we examined the cell-type-specific ultrastructure of proximal processes from the soma of parvalbumin-positive (PV+) and somatostatin-positive (SST+) GABAergic neurons in comparison with a pyramidal neuron in the mouse primary visual cortex (V1), using serial block-face scanning electron microscopy. Interestingly, each type of neuron organizes excitatory and inhibitory synapses in a unique way. First, we found that a subset of SST+ neurons are spiny, having spines on both soma and dendrites. Each of those spines has a highly complicated structure that has up to eight synaptic inputs. Next, the PV+ and SST+ neurons receive more robust excitatory inputs to their perisoma than does the pyramidal neuron. Notably, excitatory synapses on GABAergic neurons were often multiple-synapse boutons, making another synapse on distal dendrites. On the other hand, inhibitory synapses near the soma were often single-targeting multiple boutons. Collectively, our data demonstrate that synaptic inputs near the soma are differentially organized across cell types and form a network that balances inhibition and excitation in the V1.

Layer-Dependent Short-Term Synaptic Plasticity Between Excitatory Neurons in the C2 Barrel Column of Mouse Primary Somatosensory Cortex.

Neurons process information through spatiotemporal integration of synaptic input. Synaptic transmission between any given pair of neurons is typically a dynamic process with presynaptic action potentials (APs) evoking depressing or facilitating postsynaptic potentials when presynaptic APs occur within hundreds of milliseconds of each other. In order to understand neocortical function, it is therefore important to investigate such short-term synaptic plasticity at synapses between different types of neocortical neurons. Here, we examine short-term synaptic dynamics between excitatory neurons in different layers of the mouse C2 barrel column through in vitro whole-cell recordings. We find layer-dependent short-term plasticity, with depression being dominant at many synaptic connections. Interestingly, however, presynaptic layer 2 neurons predominantly give rise to facilitating excitatory synaptic output at short interspike intervals of 10 and 30 ms. Previous studies have found prominent burst firing of excitatory neurons in supragranular layers of awake mice. The facilitation we observed in the synaptic output of layer 2 may, therefore, be functionally relevant, possibly serving to enhance the postsynaptic impact of burst firing.

Optogenetic Dissection of the Basal Forebrain Neuromodulatory Control of Cortical Activation, Plasticity, and Cognition.

The basal forebrain (BF) houses major ascending projections to the entire neocortex that have long been implicated in arousal, learning, and attention. The disruption of the BF has been linked with major neurological disorders, such as coma and Alzheimer's disease, as well as in normal cognitive aging. Although it is best known for its cholinergic neurons, the BF is in fact an anatomically and neurochemically complex structure. Recent studies using transgenic mouse lines to target specific BF cell types have led to a renaissance in the study of the BF and are beginning to yield new insights about cell-type-specific circuit mechanisms during behavior. These approaches enable us to determine the behavioral conditions under which cholinergic and noncholinergic BF neurons are activated and how they control cortical processing to influence behavior. Here we discuss recent advances that have expanded our knowledge about this poorly understood brain region and laid the foundation for future cell-type-specific manipulations to modulate arousal, attention, and cortical plasticity in neurological disorders. SIGNIFICANCE STATEMENT: Although the basal forebrain is best known for, and often equated with, acetylcholine-containing neurons that provide most of the cholinergic innervation of the neocortex, it is in fact an anatomically and neurochemically complex structure. Recent studies using transgenic mouse lines to target specific cell types in the basal forebrain have led to a renaissance in this field and are beginning to dissect circuit mechanisms in the basal forebrain during behavior. This review discusses recent advances in the roles of basal forebrain cholinergic and noncholinergic neurons in cognition via their dynamic modulation of cortical activity.

Parallel pathways from motor and somatosensory cortex for controlling whisker movements in mice.

Mice can gather tactile sensory information by actively moving their whiskers to palpate objects in their immediate surroundings. Whisker sensory perception therefore requires integration of sensory and motor information, which occurs prominently in the neocortex. The signalling pathways from the neocortex for controlling whisker movements are currently poorly understood in mice. Here, we delineate two pathways, one originating from primary whisker somatosensory cortex (wS1) and the other from whisker motor cortex (wM1), that control qualitatively distinct movements of contralateral whiskers. Optogenetic stimulation of wS1 drove retraction of contralateral whiskers while stimulation of wM1 drove rhythmic whisker protraction. To map brainstem pathways connecting these cortical areas to whisker motor neurons, we used a combination of anterograde tracing using adenoassociated virus injected into neocortex and retrograde tracing using monosynaptic rabies virus injected into whisker muscles. Our data are consistent with wS1 driving whisker retraction by exciting glutamatergic premotor neurons in the rostral spinal trigeminal interpolaris nucleus, which in turn activate the motor neurons innervating the extrinsic retractor muscle nasolabialis. The rhythmic whisker protraction evoked by wM1 stimulation might be driven by excitation of excitatory and inhibitory premotor neurons in the brainstem reticular formation innervating both intrinsic and extrinsic muscles. Our data therefore begin to unravel the neuronal circuits linking the neocortex to whisker motor neurons.

Imaging the Dynamics of Neocortical Population Activity in Behaving and Freely Moving Mammals.

The development of functional imaging techniques applicable to neuroscience and covering a wide range of spatial and temporal scales has greatly facilitated the exploration of the relationships between cognition, behaviour and electrical brain activity. For mammals, the neocortex plays a particularly profound role in generating sensory perception, controlling voluntary movement, higher cognitive functions and planning goal-directed behaviours. Since these remarkable functions of the neocortex cannot be explored in simple model preparations or in anesthetised animals, the neural basis of behaviour must be explored in awake behaving subjects. Because neocortical function is highly distributed across many rapidly interacting regions, it is essential to measure spatiotemporal dynamics of cortical activity in real-time. Extensive work in anesthetised mammals has shown that in vivo Voltage-Sensitive Dye Imaging (VSDI) reveals the neocortical population membrane potential dynamics at millisecond temporal resolution and subcolumnar spatial resolution. Here, we describe recent advances indicating that VSDI is also already well-developed for exploring cortical function in behaving monkeys and mice. The first animal model, the non-human primate, is well-suited for fundamental exploration of higher-level cognitive function and behavior. The second animal model, the mouse, benefits from a rich arsenal of molecular and genetic technologies. In the monkey, imaging from the same patch of cortex, repeatedly, is feasible for a long period of time, up to a year. In the rodent, VSDI is applicable to freely moving and awake head-restrained mice. Interactions between different cortical areas and different cortical columns can therefore now be dynamically mapped through VSDI and related to the corresponding behaviour. Thus by applying VSDI to mice and monkeys one can begin to explore how behaviour emerges from neuronal activity in neuronal networks residing in different cortical areas.

Internal brain state regulates membrane potential synchrony in barrel cortex of behaving mice.

Internal brain states form key determinants for sensory perception, sensorimotor coordination and learning. A prominent reflection of different brain states in the mammalian central nervous system is the presence of distinct patterns of cortical synchrony, as revealed by extracellular recordings of the electroencephalogram, local field potential and action potentials. Such temporal correlations of cortical activity are thought to be fundamental mechanisms of neuronal computation. However, it is unknown how cortical synchrony is reflected in the intracellular membrane potential (V(m)) dynamics of behaving animals. Here we show, using dual whole-cell recordings from layer 2/3 primary somatosensory barrel cortex in behaving mice, that the V(m) of nearby neurons is highly correlated during quiet wakefulness. However, when the mouse is whisking, an internally generated state change reduces the V(m) correlation, resulting in a desynchronized local field potential and electroencephalogram. Action potential activity was sparse during both quiet wakefulness and active whisking. Single action potentials were driven by a large, brief and specific excitatory input that was not present in the V(m) of neighbouring cells. Action potential initiation occurs with a higher signal-to-noise ratio during active whisking than during quiet periods. Therefore, we show that an internal brain state dynamically regulates cortical membrane potential synchrony during behaviour and defines different modes of cortical processing.

Dopamine dynamics in nucleus accumbens across reward-based learning of goal-directed whisker-to-lick sensorimotor transformations in mice.

The synaptic and neuronal circuit mechanisms underlying reward-based learning remain to be fully determined. In the mammalian brain, dopamine release in nucleus accumbens is thought to contribute importantly to reward signals for learning and promoting synaptic plasticity. Here, through longitudinal fiber photometry of a genetically-encoded fluorescent sensor, we investigated dopamine signals in the nucleus accumbens of thirsty head-restrained mice as they learned to lick a liquid reward spout in response to single deflections of the C2 whisker across varying reward contingencies. Reward delivery triggered by well-timed licking drove fast transient dopamine increases in nucleus accumbens. On the other hand, unrewarded licking was overall associated with reduced dopamine sensor fluorescence, especially in trials where reward was unexpectedly omitted. The dopamine reward signal upon liquid delivery decreased within individual sessions as mice became sated. As mice learned to lick the reward spout in response to whisker deflection, a fast transient sensory-evoked dopamine signal developed, correlating with the learning of the whisker detection task across consecutive training days, as well as within single learning sessions. The well-defined behavioral paradigm involving a unitary stimulus of a single whisker as a reward-predicting cue along with precisely quantified licking allows untangling of sensory, motor and reward-related dopamine signals and how they evolve across the learning of whisker-dependent goal-directed sensorimotor transformations. Our longitudinal measurements of dopamine dynamics across reward-based learning are overall consistent with the hypothesis that dopamine could play an important role as a reward signal for reinforcement learning.

Cell class-specific long-range axonal projections of neurons in mouse whisker-related somatosensory cortices.

Long-range axonal projections of diverse classes of neocortical excitatory neurons likely contribute to brain-wide interactions processing sensory, cognitive and motor signals. Here, we performed light-sheet imaging of fluorescently labeled axons from genetically defined neurons located in posterior primary somatosensory barrel cortex and supplemental somatosensory cortex. We used convolutional networks to segment axon-containing voxels and quantified their distribution within the Allen Mouse Brain Atlas Common Coordinate Framework. Axonal density was analyzed for different classes of glutamatergic neurons using transgenic mouse lines selectively expressing Cre recombinase in layer 2/3 intratelencephalic projection neurons (Rasgrf2-dCre), layer 4 intratelencephalic projection neurons (Scnn1a-Cre), layer 5 intratelencephalic projection neurons (Tlx3-Cre), layer 5 pyramidal tract projection neurons (Sim1-Cre), layer 5 projection neurons (Rbp4-Cre), and layer 6 corticothalamic neurons (Ntsr1-Cre). We found distinct axonal projections from the different neuronal classes to many downstream brain areas, which were largely similar for primary and supplementary somatosensory cortices. Functional connectivity maps obtained from optogenetic activation of sensory cortex and wide-field imaging revealed topographically organized evoked activity in frontal cortex with neurons located more laterally in somatosensory cortex signaling to more anteriorly located regions in motor cortex, consistent with the anatomical projections. The current methodology therefore appears to quantify brain-wide axonal innervation patterns supporting brain-wide signaling.

Distributed and specific encoding of sensory, motor, and decision information in the mouse neocortex during goal-directed behavior.

Goal-directed behaviors involve coordinated activity in many cortical areas, but whether the encoding of task variables is distributed across areas or is more specifically represented in distinct areas remains unclear. Here, we compared representations of sensory, motor, and decision information in the whisker primary somatosensory cortex, medial prefrontal cortex, and tongue-jaw primary motor cortex in mice trained to lick in response to a whisker stimulus with mice that were not taught this association. Irrespective of learning, properties of the sensory stimulus were best encoded in the sensory cortex, whereas fine movement kinematics were best represented in the motor cortex. However, movement initiation and the decision to lick in response to the whisker stimulus were represented in all three areas, with decision neurons in the medial prefrontal cortex being more selective, showing minimal sensory responses in miss trials and motor responses during spontaneous licks. Our results reconcile previous studies indicating highly specific vs. highly distributed sensorimotor processing.

Petilla terminology: nomenclature of features of GABAergic interneurons of the cerebral cortex.

Neuroscience produces a vast amount of data from an enormous diversity of neurons. A neuronal classification system is essential to organize such data and the knowledge that is derived from them. Classification depends on the unequivocal identification of the features that distinguish one type of neuron from another. The problems inherent in this are particularly acute when studying cortical interneurons. To tackle this, we convened a representative group of researchers to agree on a set of terms to describe the anatomical, physiological and molecular features of GABAergic interneurons of the cerebral cortex. The resulting terminology might provide a stepping stone towards a future classification of these complex and heterogeneous cells. Consistent adoption will be important for the success of such an initiative, and we also encourage the active involvement of the broader scientific community in the dynamic evolution of this project.

Membrane potential dynamics of excitatory and inhibitory neurons in mouse barrel cortex during active whisker sensing.

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 a basis for constraining future computational models of neocortical function.

Striatal Dopamine Signals and Reward Learning.

We are constantly bombarded by sensory information and constantly making decisions on how to act. In order to optimally adapt behavior, we must judge which sequences of sensory inputs and actions lead to successful outcomes in specific circumstances. Neuronal circuits of the basal ganglia have been strongly implicated in action selection, as well as the learning and execution of goal-directed behaviors, with accumulating evidence supporting the hypothesis that midbrain dopamine neurons might encode a reward signal useful for learning. Here, we review evidence suggesting that midbrain dopaminergic neurons signal reward prediction error, driving synaptic plasticity in the striatum underlying learning. We focus on phasic increases in action potential firing of midbrain dopamine neurons in response to unexpected rewards. These dopamine neurons prominently innervate the dorsal and ventral striatum. In the striatum, the released dopamine binds to dopamine receptors, where it regulates the plasticity of glutamatergic synapses. The increase of striatal dopamine accompanying an unexpected reward activates dopamine type 1 receptors (D1Rs) initiating a signaling cascade that promotes long-term potentiation of recently active glutamatergic input onto striatonigral neurons. Sensorimotor-evoked glutamatergic input, which is active immediately before reward delivery will thus be strengthened onto neurons in the striatum expressing D1Rs. In turn, these neurons cause disinhibition of brainstem motor centers and disinhibition of the motor thalamus, thus promoting motor output to reinforce rewarded stimulus-action outcomes. Although many details of the hypothesis need further investigation, altogether, it seems likely that dopamine signals in the striatum might underlie important aspects of goal-directed reward-based learning.

Neural mechanisms underlying uninstructed orofacial movements during reward-based learning behaviors.

During reward-based learning tasks, animals make orofacial movements that globally influence brain activity at the timings of reward expectation and acquisition. These orofacial movements are not explicitly instructed and typically appear along with goal-directed behaviors. Here, we show that reinforcing optogenetic stimulation of dopamine neurons in the ventral tegmental area (oDAS) in mice is sufficient to induce orofacial movements in the whiskers and nose without accompanying goal-directed behaviors. Pavlovian conditioning with a sensory cue and oDAS elicited cue-locked and oDAS-aligned orofacial movements, which were distinguishable by a machine-learning model. Inhibition or knockout of dopamine D1 receptors in the nucleus accumbens inhibited oDAS-induced motion but spared cue-locked motion, suggesting differential regulation of these two types of orofacial motions. In contrast, inactivation of the whisker primary motor cortex (wM1) abolished both types of orofacial movements. We found specific neuronal populations in wM1 representing either oDAS-aligned or cue-locked whisker movements. Notably, optogenetic stimulation of wM1 neurons successfully replicated these two types of movements. Our results thus suggest that accumbal D1-receptor-dependent and -independent neuronal signals converge in the wM1 for facilitating distinct uninstructed orofacial movements during a reward-based learning task.

Microcircuits of excitatory and inhibitory neurons in layer 2/3 of mouse barrel cortex.

Synaptic interactions between nearby excitatory and inhibitory neurons in the neocortex are thought to play fundamental roles in sensory processing. Here, we have combined optogenetic stimulation, whole cell recordings, and computational modeling to define key functional microcircuits within layer 2/3 of mouse primary somatosensory barrel cortex. In vitro optogenetic stimulation of excitatory layer 2/3 neurons expressing channelrhodopsin-2 evoked a rapid sequence of excitation followed by inhibition. Fast-spiking (FS) GABAergic neurons received large-amplitude, fast-rising depolarizing postsynaptic potentials, often driving action potentials. In contrast, the same optogenetic stimulus evoked small-amplitude, subthreshold postsynaptic potentials in excitatory and non-fast-spiking (NFS) GABAergic neurons. To understand the synaptic mechanisms underlying this network activity, we investigated unitary synaptic connectivity through multiple simultaneous whole cell recordings. FS GABAergic neurons received unitary excitatory postsynaptic potentials with higher probability, larger amplitudes, and faster kinetics compared with NFS GABAergic neurons and other excitatory neurons. Both FS and NFS GABAergic neurons evoked robust inhibition on postsynaptic layer 2/3 neurons. A simple computational model based on the experimentally determined electrophysiological properties of the different classes of layer 2/3 neurons and their unitary synaptic connectivity accounted for key aspects of the network activity evoked by optogenetic stimulation, including the strong recruitment of FS GABAergic neurons acting to suppress firing of excitatory neurons. We conclude that FS GABAergic neurons play an important role in neocortical microcircuit function through their strong local synaptic connectivity, which might contribute to driving sparse coding in excitatory layer 2/3 neurons of mouse barrel cortex in vivo.

Parameter extraction and classification of three cortical neuron types reveals two distinct adaptation mechanisms.

Cortical information processing originates from the exchange of action potentials between many cell types. To capture the essence of these interactions, it is of critical importance to build mathematical models that reflect the characteristic features of spike generation in individual neurons. We propose a framework to automatically extract such features from current-clamp experiments, in particular the passive properties of a neuron (i.e., membrane time constant, reversal potential, and capacitance), the spike-triggered adaptation currents, as well as the dynamics of the action potential threshold. The stochastic model that results from our maximum likelihood approach accurately predicts the spike times, the subthreshold voltage, the firing patterns, and the type of frequency-current curve. Extracting the model parameters for three cortical cell types revealed that cell types show highly significant differences in the time course of the spike-triggered currents and moving threshold, that is, in their adaptation and refractory properties but not in their passive properties. In particular, GABAergic fast-spiking neurons mediate weak adaptation through spike-triggered currents only, whereas regular spiking excitatory neurons mediate adaptation with both moving threshold and spike-triggered currents. GABAergic nonfast-spiking neurons combine the two distinct adaptation mechanisms with reduced strength. Differences between cell types are large enough to enable automatic classification of neurons into three different classes. Parameter extraction is performed for individual neurons so that we find not only the mean parameter values for each neuron type but also the spread of parameters within a group of neurons, which will be useful for future large-scale computer simulations.

Cortical sensory processing across motivational states during goal-directed behavior.

Behavioral states can influence performance of goal-directed sensorimotor tasks. Yet, it is unclear how altered neuronal sensory representations in these states relate to task performance and learning. We trained water-restricted mice in a two-whisker discrimination task to study cortical circuits underlying perceptual decision-making under different levels of thirst. We identified somatosensory cortices as well as the premotor cortex as part of the circuit necessary for task execution. Two-photon calcium imaging in these areas identified populations selective to sensory or motor events. Analysis of task performance during individual sessions revealed distinct behavioral states induced by decreasing levels of thirst-related motivation. Learning was better explained by improvements in motivational state control rather than sensorimotor association. Whisker sensory representations in the cortex were altered across behavioral states. In particular, whisker stimuli could be better decoded from neuronal activity during high task performance states, suggesting that state-dependent changes of sensory processing influence decision-making.