Introduction The intricate microcircuitry of the cerebral cortex is thought to be a critical substrate from which arise the impressive capabilities of the mammalian brain. Until now, our knowledge of the stereotypical connectivity in neocortical microcircuits has been pieced together from individual studies of the connectivity between small numbers of neuronal cell types. Here, we provide unbiased, large-scale profiling of neuronal cell types and connections to reveal the essential building blocks of the cortex and the principles governing their assembly into cortical circuits. Using advanced techniques for tissue slicing, multiple simultaneous whole-cell recording, and morphological reconstruction, we are able to provide a comprehensive view of the connectivity between diverse types of neurons, particularly among types of γ-aminobutyric acid–releasing (GABAergic) interneurons, in the adult animal. Rationale We took advantage of a method for preparing high-quality slices of adult tissue and combined this technique with octuple simultaneous, whole-cell recordings followed by an improved staining method that allowed detailed recovery of axonal and dendritic arbor morphology. These data allowed us to perform a census of morphologically and electrophysiologically defined neuronal types (primarily GABAergic interneurons) in neocortical layers 1, 2/3, and 5 (L1, L23, and L5, respectively) and to observe their connectivity patterns in adult animals. Results Our large-scale, comprehensive profiling of neocortical neurons differentiated 15 major types of interneurons, in addition to two lamina-defined types of pyramidal neurons (L23 and L5). Cortical interneurons comprise two types in L1 (eNGC and SBC-like), seven in L23 (L23MC, L23NGC, BTC, BPC, DBC, L23BC, and ChC), and six in L5 (L5MC, L5NGC, L5BC, SC, HEC, and DC) (see the figure). Each type has stereotypical electrophysiological properties and morphological features and can be differentiated from all others by cell type-specific axonal geometry and axonal projection patterns. Importantly, each type of neuron has its own characteristic input-output connectivity profile, connecting with other constituent neuronal types with varying degrees of specificity in postsynaptic targets, laminar location, and synaptic characteristics. Despite specific connection patterns for each cell type, we found that a small number of simple connectivity motifs are repeated across layers and cell types defining a canonical cortical microcircuit. Conclusion Our comprehensive profiling of neuronal cell types and connections in adult neocortex provides the most complete wiring diagram of neocortical microcircuits to date. Compared with current genetic labels for cell class, which paint the cortex in broad strokes, our analysis of morphological and electrophysiological properties revealed new cell classes and allowed us to derive a small number of simple connectivity rules that were repeated across layers and cell types. This detailed blueprint of cortical wiring should aid efforts to i...

Neural responses are modulated by brain state, which varies with arousal, attention, and behavior. In mice, running and whisking desynchronize the cortex and enhance sensory responses, but the quiescent periods between bouts of exploratory behaviors have not been well-studied. We found that these periods of “quiet wakefulness” were characterized by state fluctuations on a timescale of 1–2 seconds. Small fluctuations in pupil diameter tracked these state transitions in multiple cortical areas. During dilation, the intracellular membrane potential was desynchronized, sensory responses were enhanced, and population activity was less correlated. In contrast, constriction was characterized by increased low-frequency oscillations and higher ensemble correlations. Specific subtypes of cortical interneurons were differentially activated during dilation and constriction, consistent with their participation in the observed state changes. Pupillometry has been used to index attention and mental effort in humans, but the intracellular dynamics and differences in population activity underlying this phenomenon were previously unknown.

The state of the brain and body constantly varies on rapid and slow time scales. These variations contribute to the apparent noisiness of sensory responses at both the neural and behavioral level. Recent investigations of rapid state changes in awake, behaving animals have provided insight into the mechanisms by which optimal sensory encoding and behavioral performance are achieved. Fluctuations in state, as indexed by pupillometry, impact both the “signal” (sensory evoked response) and the “noise” (spontaneous activity) of cortical responses. By taking these fluctuations into account, neural response (co-)variability is significantly reduced, revealing the brain to be more reliable and predictable than previously thought.

Shared, trial-to-trial variability in neuronal populations has a strong impact on the accuracy of information processing in the brain. Estimates of the level of such noise correlations are diverse, ranging from 0.01 to 0.4, with little consensus on which factors account for these differences. Here we addressed one important factor that varied across studies, asking how anesthesia affects the population activity structure in macaque primary visual cortex. We found that under opioid anesthesia, activity was dominated by strong coordinated fluctuations on a timescale of 1–2 Hz, which were mostly absent in awake, fixating monkeys. Accounting for these global fluctuations markedly reduced correlations under anesthesia, matching those observed during wakefulness and reconciling earlier studies conducted under anesthesia and in awake animals. Our results show that internal signals, such as brain state transitions under anesthesia, can induce noise correlations, but can also be estimated and accounted for based on neuronal population activity.

Despite the importance of the mammalian neocortex for complex cognitive processes, we still lack a comprehensive description of its cellular components. To improve the classification of neuronal cell types and the functional characterization of single neurons, we present Patch-seq, a method that combines whole-cell electrophysiological patch-clamp recordings, single-cell RNA-sequencing and morphological characterization. Following electrophysiological characterization, cell contents are aspirated through the patch-clamp pipette and prepared for RNA-sequencing. Using this approach, we generate electrophysiological and molecular profiles of 58 neocortical cells and show that gene expression patterns can be used to infer the morphological and physiological properties such as axonal arborization and action potential amplitude of individual neurons. Our results shed light on the molecular underpinnings of neuronal diversity and suggest that Patch-seq can facilitate the classification of cell types in the nervous system.