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Two-photon calcium imaging and electron microscopy were used to explore the relationship between structure and function in mouse primary visual cortex, showing that layer 2/3 neurons are connected in subnetworks, that pyramidal neurons with similar orientation selectivity preferentially form synapses with each other, and that neurons with similar orientation tuning form larger synapses; this study exemplifies functional connectomics as a powerful method for studying the organizational logic of cortical networks. The connectomics of excitatory cortical networks To explore the relationship between structure and function in cortical networks, Wei-Chung Allen Lee and colleagues combined two-photon calcium imaging and electron microscopy in mouse primary visual cortex. They found that layer 2/3 neurons are organized into subnetworks, that pyramidal neurons with similar orientation selectivity preferentially form synapses with each other, and that neurons with similar orientation tuning form larger synapses. This study exemplifies functional connectomics as a powerful method for studying the organizational logic of cortical networks. Circuits in the cerebral cortex consist of thousands of neurons connected by millions of synapses. A precise understanding of these local networks requires relating circuit activity with the underlying network structure. For pyramidal cells in superficial mouse visual cortex (V1), a consensus is emerging that neurons with similar visual response properties excite each other 1 , 2 , 3 , 4 , 5 , but the anatomical basis of this recurrent synaptic network is unknown. Here we combined physiological imaging and large-scale electron microscopy to study an excitatory network in V1. We found that layer 2/3 neurons organized into subnetworks defined by anatomical connectivity, with more connections within than between groups. More specifically, we found that pyramidal neurons with similar orientation selectivity preferentially formed synapses with each other, despite the fact that axons and dendrites of all orientation selectivities pass near (<5 μm) each other with roughly equal probability. Therefore, we predict that mechanisms of functionally specific connectivity take place at the length scale of spines. Neurons with similar orientation tuning formed larger synapses, potentially enhancing the net effect of synaptic specificity. With the ability to study thousands of connections in a single circuit, functional connectomics is proving a powerful method to uncover the organizational logic of cortical networks.

Many animals use visual information to navigate 1 – 4 , but how such information is encoded and integrated by the navigation system remains incompletely understood. In Drosophila melanogaster , EPG neurons in the central complex compute the heading direction 5 by integrating visual input from ER neurons 6 – 12 , which are part of the anterior visual pathway (AVP) 10 , 13 – 16 . Here we densely reconstruct all neurons in the AVP using electron-microscopy data 17 . The AVP comprises four neuropils, sequentially linked by three major classes of neurons: MeTu neurons 10 , 14 , 15 , which connect the medulla in the optic lobe to the small unit of the anterior optic tubercle (AOTUsu) in the central brain; TuBu neurons 9 , 16 , which connect the AOTUsu to the bulb neuropil; and ER neurons 6 – 12 , which connect the bulb to the EPG neurons. On the basis of morphologies, connectivity between neural classes and the locations of synapses, we identify distinct information channels that originate from four types of MeTu neurons, and we further divide these into ten subtypes according to the presynaptic connections in the medulla and the postsynaptic connections in the AOTUsu. Using the connectivity of the entire AVP and the dendritic fields of the MeTu neurons in the optic lobes, we infer potential visual features and the visual area from which any ER neuron receives input. We confirm some of these predictions physiologically. These results provide a strong foundation for understanding how distinct sensory features can be extracted and transformed across multiple processing stages to construct higher-order cognitive representations. Electron-microscopy data are used to reconstruct the neurons that make up the anterior visual pathway in the Drosophila brain, providing insight into how visual features are encoded to guide navigation.

Visual input is often noisy and discontinuous, even though the physical environment is generally stable. The authors show that the visual system trades off change sensitivity to capitalize on physical continuity via serial dependence: present perception is biased toward past visual input. This bias is modulated by attention and governed by a spatiotemporally-tuned operator, a continuity field. Visual input often arrives in a noisy and discontinuous stream, owing to head and eye movements, occlusion, lighting changes, and many other factors. Yet the physical world is generally stable; objects and physical characteristics rarely change spontaneously. How then does the human visual system capitalize on continuity in the physical environment over time? We found that visual perception in humans is serially dependent, using both prior and present input to inform perception at the present moment. Using an orientation judgment task, we found that, even when visual input changed randomly over time, perceived orientation was strongly and systematically biased toward recently seen stimuli. Furthermore, the strength of this bias was modulated by attention and tuned to the spatial and temporal proximity of successive stimuli. These results reveal a serial dependence in perception characterized by a spatiotemporally tuned, orientation-selective operator—which we call a continuity field—that may promote visual stability over time.

A catalogue of neuronal cell types has often been called a ‘parts list’ of the brain 1 , and regarded as a prerequisite for understanding brain function 2 , 3 . In the optic lobe of Drosophila , rules of connectivity between cell types have already proven to be essential for understanding fly vision 4 , 5 . Here we analyse the fly connectome to complete the list of cell types intrinsic to the optic lobe, as well as the rules governing their connectivity. Most new cell types contain 10 to 100 cells, and integrate information over medium distances in the visual field. Some existing type families (Tm, Li, and LPi) 6 – 10 at least double in number of types. A new serpentine medulla (Sm) interneuron family contains more types than any other. Three families of cross-neuropil types are revealed. The consistency of types is demonstrated by analysing the distances in high-dimensional feature space, and is further validated by algorithms that select small subsets of discriminative features. We use connectivity to hypothesize about the functional roles of cell types in motion, object and colour vision. Connectivity with ‘boundary types’ that straddle the optic lobe and central brain is also quantified. We showcase the advantages of connectomic cell typing: complete and unbiased sampling, a rich array of features based on connectivity and reduction of the connectome to a substantially simpler wiring diagram of cell types, with immediate relevance for brain function and development. An analysis of the Drosophila connectome yields all cell types intrinsic to the optic lobe, and their rules of connectivity.

Animal behaviour arises from computations in neuronal circuits, but our understanding of these computations has been frustrated by the lack of detailed synaptic connection maps, or connectomes. For example, despite intensive investigations over half a century, the neuronal implementation of local motion detection in the insect visual system remains elusive. Here we develop a semi-automated pipeline using electron microscopy to reconstruct a connectome, containing 379 neurons and 8,637 chemical synaptic contacts, within the Drosophila optic medulla. By matching reconstructed neurons to examples from light microscopy, we assigned neurons to cell types and assembled a connectome of the repeating module of the medulla. Within this module, we identified cell types constituting a motion detection circuit, and showed that the connections onto individual motion-sensitive neurons in this circuit were consistent with their direction selectivity. Our results identify cellular targets for future functional investigations, and demonstrate that connectomes can provide key insights into neuronal computations. Reconstruction of a connectome within the fruitfly visual medulla, containing more than 300 neurons and over 8,000 chemical synapses, reveals a candidate motion detection circuit; such a circuit operates by combining displaced visual inputs, an operation consistent with correlation based motion detection. Visual system connectomics — from insects to mammals Three papers in this issue of Nature use the retina as a model for mapping neuronal circuits from the level of individual synaptic contacts to the long-range scale of dendritic interactions. Helmstaedter et al . used electron microscopy to map a mammalian retinal circuit of close to a thousand neurons. The work reveals a new type of retinal bipolar neuron and suggests functional mechanisms for known visual computations. The other two groups study the detection of visual motion in the Drosophila visual system — a classic neural computation model. Takemura et al . used semi-automated electron microscopy to reconstruct the basic connectome (8,637 chemical synapses among 379 neurons) of Drosophila 's optic medulla. Their results reveal a candidate motion detection circuit with a wiring plan consistent with direction selectivity. Maisak et al . used calcium imaging to show that T4 and T5 neurons are divided into specific subpopulations responding to motion in four cardinal directions, and are specific to 'ON' versus 'OFF' edges, respectively.

Social affiliation emerges from individual-level behavioural rules that are driven by conspecific signals 1 – 5 . Long-distance attraction and short-distance repulsion, for example, are rules that jointly set a preferred interanimal distance in swarms 6 – 8 . However, little is known about their perceptual mechanisms and executive neural circuits 3 . Here we trace the neuronal response to self-like biological motion 9 , 10 , a visual trigger for affiliation in developing zebrafish 2 , 11 . Unbiased activity mapping and targeted volumetric two-photon calcium imaging revealed 21 activity hotspots distributed throughout the brain as well as clustered biological-motion-tuned neurons in a multimodal, socially activated nucleus of the dorsal thalamus. Individual dorsal thalamus neurons encode local acceleration of visual stimuli mimicking typical fish kinetics but are insensitive to global or continuous motion. Electron microscopic reconstruction of dorsal thalamus neurons revealed synaptic input from the optic tectum and projections into hypothalamic areas with conserved social function 12 – 14 . Ablation of the optic tectum or dorsal thalamus selectively disrupted social attraction without affecting short-distance repulsion. This tectothalamic pathway thus serves visual recognition of conspecifics, and dissociates neuronal control of attraction from repulsion during social affiliation, revealing a circuit underpinning collective behaviour. A tectothalamic pathway for social affiliation in developing zebrafish dissociates neuronal control of attraction from repulsion during affiliation, revealing a circuit underpinning of collective behaviour

Human visual recognition activates a dense network of overlapping feedforward and recurrent neuronal processes, making it hard to disentangle processing in the feedforward from the feedback direction. Here, we used ultra-rapid serial visual presentation to suppress sustained activity that blurs the boundaries of processing steps, enabling us to resolve two distinct stages of processing with MEG multivariate pattern classification. The first processing stage was the rapid activation cascade of the bottom-up sweep, which terminated early as visual stimuli were presented at progressively faster rates. The second stage was the emergence of categorical information with peak latency that shifted later in time with progressively faster stimulus presentations, indexing time-consuming recurrent processing. Using MEG-fMRI fusion with representational similarity, we localized recurrent signals in early visual cortex. Together, our findings segregated an initial bottom-up sweep from subsequent feedback processing, and revealed the neural signature of increased recurrent processing demands for challenging viewing conditions. The human brain can interpret the visual world in less than the blink of an eye. Specialized brain regions process different aspects of visual objects. These regions form a hierarchy. Areas at the base of the hierarchy process simple features such as lines and angles. They then pass this information onto areas above them, which process more complex features, such as shapes. Eventually the area at the top of the hierarchy identifies the object. But information does not only flow from the bottom of the hierarchy to the top. It also flows from top to bottom. The latter is referred to as feedback activity, but its exact role remains unclear. Mohsenzadeh et al. used two types of imaging to map brain activity in space and time in healthy volunteers performing a visual task. The volunteers had to decide whether a series of images that flashed up briefly on a screen included a face or not. The results showed that the brain adapts its visual processing strategy to suit the viewing conditions. They also revealed three key principles for how the brain recognizes visual objects. First, if early visual information is incomplete – for example, because the images appeared only briefly – higher regions of the hierarchy spend more time processing the images. Second, when visual information is incomplete, higher regions of the hierarchy send more feedback down to lower regions. This leads to delays in identifying the object. And third, lower regions in the hierarchy – known collectively as early visual cortex – process the feedback signals. This processing takes place at the same time as the higher levels identify the object. Knowing the role of feedback is critical to understanding how the visual system works. The next step is to develop computer models of visual processing. The current findings on the role of feedback should prove useful in designing such models. These might ultimately pave the way to developing treatments for visual impairments caused by damage to visual areas of the brain.

The functional characteristics of the mouse visual system have not previously been well explored using fMRI. In this research, we examined 9.4 T BOLD fMRI responses to visual stimuli of varying pulse durations (1 – 50 ms) and temporal frequencies (1 – 10 Hz) under ketamine and xylazine anesthesia, and compared fMRI responses of anesthetized and awake mice. Under anesthesia, significant positive BOLD responses were detected bilaterally in the major structures of the visual pathways, including the dorsal lateral geniculate nuclei, superior colliculus, lateral posterior nucleus of thalamus, primary visual area, and higher-order visual area. BOLD responses increased slightly with pulse duration, were maximal at 3 – 5 Hz stimulation, and significantly decreased at 10 Hz, which were all consistent with previous neurophysiological findings. When the mice were awake, the BOLD fMRI response was faster in all active regions and stronger in the subcortical areas compared with the anesthesia condition. In the V1, the BOLD response was biphasic for 5 Hz stimulation and negative for 10 Hz stimulation under wakefulness, whereas prolonged positive BOLD responses were observed at both frequencies under anesthesia. Unexpected activation was detected in the extrastriate postrhinal area and non-visual subiculum complex under anesthesia, but not under wakefulness. Widespread positive BOLD activity under anesthesia likely results from the disinhibition and sensitization of excitatory neurons induced by ketamine. Overall, fMRI can be a viable tool for mapping brain-wide functional networks.