We present a novel slit scanning confocal microscope with a CCD camera image sensor and a virtual slit aperture for descanning that can be adjusted during post-processing. A very efficient data structure and mathematical criteria for aligning the virtual aperture guarantee the ease of use. We further introduce a method to reduce the anisotropic lateral resolution of slit scanning microscopes. System performance is evaluated against a spinning disk confocal microscope on identical specimens. The virtual slit scanning microscope works as the spinning disk type and outperforms on thick specimens.

Cortical computation arises from the interaction of multiple neuronal types, including pyramidal (Pyr) cells and interneurons expressing Sst, Vip, or Pvalb. To study the circuit underlying such interactions, we imaged these four types of cells in mouse primary visual cortex(V1). Our recordings in darkness were consistent with a "disinhibitory" model in which locomotion activates Vip cells, thus inhibiting Sst cells and disinhibiting Pyr cells. However, the disinhibitory model failed when visual stimuli were present: locomotion increased Sst cell responses to large stimuli and Vip cell responses to small stimuli. A recurrent network model successfully predicted each cell type's activity from the measured activity of other types. Capturing the effects of locomotion, however, required allowing it to increase feedforward synaptic weights and modulate recurrent weights. This network model summarizes interneuron interactions and suggests that locomotion may alter cortical computation by changing effective synaptic connectivity.

Populations of neurons in primary visual cortex (V1) transform direct thalamic inputs into a cortical representation which acquires new spatio-temporal properties. One of these properties, motion selectivity, has not been strongly tied to putative neural mechanisms, and its origins remain poorly understood. Here we propose that motion selectivity is acquired through the recurrent mechanisms of a network of strongly connected neurons. We first show that a bank of V1 spatiotemporal receptive fields can be generated accurately by a network which receives only instantaneous inputs from the retina. The temporal structure of the receptive fields is generated by the long timescale dynamics associated with the high magnitude eigenvalues of the recurrent connectivity matrix. When these eigenvalues have complex parts, they generate receptive fields that are inseparable in time and space, such as those tuned to motion direction. We also show that the recurrent connectivity patterns can be learnt directly from the statistics of natural movies using a temporally-asymmetric Hebbian learning rule. Probed with drifting grating stimuli and moving bars, neurons in the model show patterns of responses analogous to those of direction-selective simple cells in primary visual cortex. These computations are enabled by a specific pattern of recurrent connections, that can be tested by combining connectome reconstructions with functional recordings.

As an animal translates through the world, its eyes will experience a radiating pattern of optic flow in which there is a focus of expansion directly in front and a focus of contraction behind. For flying fruit flies, recent experiments indicate that flies actively steer away from patterns of expansion. Whereas such a reflex makes sense for avoiding obstacles, it presents a paradox of sorts because an insect could not navigate stably through a visual scene unless it tolerated flight towards a focus of expansion during episodes of forward translation. One possible solution to this paradox is that a fly’s behavior might change such that it steers away from strong expansion, but actively steers towards weak expansion. In this study, we use a tethered flight arena to investigate the influence of stimulus strength on the magnitude and direction of turning responses to visual expansion in flies. These experiments indicate that the expansion-avoidance behavior is speed dependent. At slower speeds of expansion, flies exhibit an attraction to the focus of expansion, whereas the behavior transforms to expansion avoidance at higher speeds. Open-loop experiments indicate that this inversion of the expansion-avoidance response depends on whether or not the head is fixed to the thorax. The inversion of the expansion-avoidance response with stimulus strength has a clear manifestation under closed-loop conditions. Flies will actively orient towards a focus of expansion at low temporal frequency but steer away from it at high temporal frequency. The change in the response with temporal frequency does not require motion stimuli directly in front or behind the fly. Animals in which the stimulus was presented within 120 deg sectors on each side consistently steered towards expansion at low temporal frequency and steered towards contraction at high temporal frequency. A simple model based on an array of Hassenstein-Reichardt type elementary movement detectors suggests that the inversion of the expansion-avoidance reflex can explain the spatial distribution of straight flight segments and collision-avoidance saccades when flies fly freely within an open circular arena.

The visual neurons of many animals process sensory input differently depending on the animal’s state of locomotion. Now, new work in Drosophila melanogaster shows that neuromodulatory neurons active during flight boost responses of neurons in the visual system.

The role of the foraging (for) gene, which encodes a cyclic guanosine-3’,5’-monophosphate (cGMP)-dependent protein kinase (PKG), in food-search behavior in Drosophila has been intensively studied. However, its functions in other complex behaviors have not been well-characterized. Here, we show experimentally in Drosophila that the for gene is required in the operant visual learning paradigm. Visual pattern memory was normal in a natural variant rover (for(R)) but was impaired in another natural variant sitter (for(S)), which has a lower PKG level. Memory defects in for(S) flies could be rescued by either constitutive or adult-limited expression of for in the fan-shaped body. Interestingly, we showed that such rescue also occurred when for was expressed in the ellipsoid body. Additionally, expression of for in the fifth layer of the fan-shaped body restored sufficient memory for the pattern parameter "elevation" but not for "contour orientation," whereas expression of for in the ellipsoid body restored sufficient memory for both parameters. Our study defines a Drosophila model for further understanding the role of cGMP-PKG signaling in associative learning/memory and the neural circuit underlying this for-dependent visual pattern memory.

Despite advances in experimental techniques and accumulation of large datasets concerning the composition and properties of the cortex, quantitative modeling of cortical circuits under in-vivo-like conditions remains challenging. Here we report and publicly release a biophysically detailed circuit model of layer 4 in the mouse primary visual cortex, receiving thalamo-cortical visual inputs. The 45,000-neuron model was subjected to a battery of visual stimuli, and results were compared to published work and new in vivo experiments. Simulations reproduced a variety of observations, including effects of optogenetic perturbations. Critical to the agreement between responses in silico and in vivo were the rules of functional synaptic connectivity between neurons. Interestingly, after extreme simplification the model still performed satisfactorily on many measurements, although quantitative agreement with experiments suffered. These results emphasize the importance of functional rules of cortical wiring and enable a next generation of data-driven models of in vivo neural activity and computations.

The ability of insects to learn and navigate to specific locations in the environment has fascinated naturalists for decades. The impressive navigational abilities of ants, bees, wasps and other insects demonstrate that insects are capable of visual place learning, but little is known about the underlying neural circuits that mediate these behaviours. Drosophila melanogaster (common fruit fly) is a powerful model organism for dissecting the neural circuitry underlying complex behaviours, from sensory perception to learning and memory. Drosophila can identify and remember visual features such as size, colour and contour orientation. However, the extent to which they use vision to recall specific locations remains unclear. Here we describe a visual place learning platform and demonstrate that Drosophila are capable of forming and retaining visual place memories to guide selective navigation. By targeted genetic silencing of small subsets of cells in the Drosophila brain, we show that neurons in the ellipsoid body, but not in the mushroom bodies, are necessary for visual place learning. Together, these studies reveal distinct neuroanatomical substrates for spatial versus non-spatial learning, and establish Drosophila as a powerful model for the study of spatial memories.

Visual features detected by the early visual system must be combined into higher order representations to guide behavioral decision. Although key developmental mechanisms that enable the separation of visual feature channels in early visual circuits have been discovered, relatively little is known about the mechanisms that underlie their convergence in later stages of visual processing. Here we explore the development of a functionally well-characterized sensorimotor circuit in Drosophila melanogaster, the convergence of visual projection neurons (VPNs) onto the dendrites of a large descending neuron called the giant fiber (GF). We find two VPNs encoding different visual features that target the same giant fiber dendrite establish their territories on the dendrite, in part, through sequential axon arrival during development prior to synaptogenesis. Physical occupancy is important to maintain territories, as we find the ablation of one VPN results in expanded dendrite territory of the remaining VPN, and that this compensation enables the GF to remain responsive to ethologically relevant visual stimuli. Our data highlight temporal mechanisms for visual feature convergence and promote the GF circuit, and the Drosophila optic glomeruli where VPN to GF connectivity resides, as an ideal developmental model for investigating complex wiring programs and plasticity in visual feature convergence.