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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...

Correlated trial-to-trial variability in the activity of cortical neurons is thought to reflect the functional connectivity of the circuit. Many cortical areas are organized into functional columns, in which neurons are believed to be densely connected and to share common input. Numerous studies report a high degree of correlated variability between nearby cells. We developed chronically implanted multitetrode arrays offering unprecedented recording quality to reexamine this question in the primary visual cortex of awake macaques. We found that even nearby neurons with similar orientation tuning show virtually no correlated variability. Our findings suggest a refinement of current models of cortical microcircuit architecture and function: Either adjacent neurons share only a few percent of their inputs or, alternatively, their activity is actively decorrelated.

Developments in microfabrication technology have enabled the production of neural electrode arrays with hundreds of closely-spaced recording sites, and electrodes with thousands of sites are currently under development. These probes in principle allow the simultaneous recording of very large numbers of neurons. However, use of this technology requires the development of techniques for decoding the spike times of the recorded neurons, from the raw data captured from the probes. Here, we present a set of novel tools to solve this problem, implemented in a suite of practical, user-friendly, open-source software. We validate these methods on data from the cortex, hippocampus, and thalamus of rat, mouse, macaque, and marmoset, demonstrating error rates as low as 5%.

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.

The amount of information encoded by networks of neurons critically depends on the correlation structure of their activity. Neurons with similar stimulus preferences tend to have higher noise correlations than others. In homogeneous populations of neurons this limited range correlation structure is highly detrimental to the accuracy of a population code. Therefore, reduced spike count correlations under attention, after adaptation or after learning have been interpreted as evidence for a more efficient population code. Here we analyze the role of limited range correlations in more realistic, heterogeneous population models. We use Fisher information and maximum likelihood decoding to show that reduced correlations do not necessarily improve encoding accuracy. In fact, in populations with more than a few hundred neurons, increasing the level of limited range correlations can substantially improve encoding accuracy. We found that this improvement results from a decrease in noise entropy that is associated with increasing correlations if the marginal distributions are unchanged. Surprisingly, for constant noise entropy and in the limit of large populations the encoding accuracy is independent of both structure and magnitude of noise correlations.

Neural Style Transfer has shown very exciting results enabling new forms of image manipulation. Here we extend the existing method to introduce control over spatial location, colour information and across spatial scale 12 . We demonstrate how this enhances the method by allowing high-resolution controlled stylisation and helps to alleviate common failure cases such as applying ground textures to sky regions. Furthermore, by decomposing style into these perceptual factors we enable the combination of style information from multiple sources to generate new, perceptually appealing styles from existing ones. We also describe how these methods can be used to more efficiently produce large size, high-quality stylisation. Finally we show how the introduced control measures can be applied in recent methods for Fast Neural Style Transfer.

The neural code is believed to have adapted to the statistical properties of the natural environment. However, the principles that govern the organization of ensemble activity in the visual cortex during natural visual input are unknown. We recorded populations of up to 500 neurons in the mouse primary visual cortex and characterized the structure of their activity, comparing responses to natural movies with those to control stimuli. We found that higher-order correlations in natural scenes induce a sparser code, in which information is encoded by reliable activation of a smaller set of neurons and can be read-out more easily. This computationally advantageous encoding for natural scenes was state-dependent and apparent only in anesthetized and active awake animals, but not during quiet wakefulness. Our results argue for a functional benefit of sparsification that could be a general principle governing the structure of the population activity throughout cortical microcircuits.