The Temporal Tuning of the Drosophila Motion Detectors Is Determined by the Dynamics of Their Input Elements.

Detecting the direction of motion contained in the visual scene is crucial for many behaviors. However, because single photoreceptors only signal local luminance changes, motion detection requires a comparison of signals from neighboring photoreceptors across time in downstream neuronal circuits. For signals to coincide on readout neurons that thus become motion and direction selective, different input lines need to be delayed with respect to each other. Classical models of motion detection rely on non-linear interactions between two inputs after different temporal filtering. However, recent studies have suggested the requirement for at least three, not only two, input signals. Here, we comprehensively characterize the spatiotemporal response properties of all columnar input elements to the elementary motion detectors in the fruit fly, T4 and T5 cells, via two-photon calcium imaging. Between these input neurons, we find large differences in temporal dynamics. Based on this, computer simulations show that only a small subset of possible arrangements of these input elements maps onto a recently proposed algorithmic three-input model in a way that generates a highly direction-selective motion detector, suggesting plausible network architectures. Moreover, modulating the motion detection system by octopamine-receptor activation, we find the temporal tuning of T4 and T5 cells to be shifted toward higher frequencies, and this shift can be fully explained by the concomitant speeding of the input elements.

Complementary mechanisms create direction selectivity in the fly.

How neurons become sensitive to the direction of visual motion represents a classic example of neural computation. Two alternative mechanisms have been discussed in the literature so far: preferred direction enhancement, by which responses are amplified when stimuli move along the preferred direction of the cell, and null direction suppression, where one signal inhibits the response to the subsequent one when stimuli move along the opposite, i.e. null direction. Along the processing chain in the Drosophila optic lobe, directional responses first appear in T4 and T5 cells. Visually stimulating sequences of individual columns in the optic lobe with a telescope while recording from single T4 neurons, we find both mechanisms at work implemented in different sub-regions of the receptive field. This finding explains the high degree of directional selectivity found already in the fly's primary motion-sensing neurons and marks an important step in our understanding of elementary motion detection.

Neural Circuit to Integrate Opposing Motions in the Visual Field.

When navigating in their environment, animals use visual motion cues as feedback signals that are elicited by their own motion. Such signals are provided by wide-field neurons sampling motion directions at multiple image points as the animal maneuvers. Each one of these neurons responds selectively to a specific optic flow-field representing the spatial distribution of motion vectors on the retina. Here, we describe the discovery of a group of local, inhibitory interneurons in the fruit fly Drosophila key for filtering these cues. Using anatomy, molecular characterization, activity manipulation, and physiological recordings, we demonstrate that these interneurons convey direction-selective inhibition to wide-field neurons with opposite preferred direction and provide evidence for how their connectivity enables the computation required for integrating opposing motions. Our results indicate that, rather than sharpening directional selectivity per se, these circuit elements reduce noise by eliminating non-specific responses to complex visual information.

Synaptic diversity enables temporal coding of coincident multisensory inputs in single neurons.

The ability of the brain to rapidly process information from multiple pathways is critical for reliable execution of complex sensory-motor behaviors, yet the cellular mechanisms underlying a neuronal representation of multimodal stimuli are poorly understood. Here we explored the possibility that the physiological diversity of mossy fiber (MF) to granule cell (GC) synapses in the mouse vestibulocerebellum may contribute to the processing of coincident multisensory information at the level of individual GCs. We found that the strength and short-term dynamics of individual MF-GC synapses can act as biophysical signatures for primary vestibular, secondary vestibular and visual input pathways. Most GCs receive inputs from different modalities, which, when coactivated, produced enhanced GC firing rates and distinct first spike latencies. Thus, pathway-specific synaptic response properties permit temporal coding of correlated multisensory inputs by single GCs, thereby enriching sensory representation and facilitating pattern separation.

Probabilistic identification of cerebellar cortical neurones across species.

Despite our fine-grain anatomical knowledge of the cerebellar cortex, electrophysiological studies of circuit information processing over the last fifty years have been hampered by the difficulty of reliably assigning signals to identified cell types. We approached this problem by assessing the spontaneous activity signatures of identified cerebellar cortical neurones. A range of statistics describing firing frequency and irregularity were then used, individually and in combination, to build Gaussian Process Classifiers (GPC) leading to a probabilistic classification of each neurone type and the computation of equi-probable decision boundaries between cell classes. Firing frequency statistics were useful for separating Purkinje cells from granular layer units, whilst firing irregularity measures proved most useful for distinguishing cells within granular layer cell classes. Considered as single statistics, we achieved classification accuracies of 72.5% and 92.7% for granular layer and molecular layer units respectively. Combining statistics to form twin-variate GPC models substantially improved classification accuracies with the combination of mean spike frequency and log-interval entropy offering classification accuracies of 92.7% and 99.2% for our molecular and granular layer models, respectively. A cross-species comparison was performed, using data drawn from anaesthetised mice and decerebrate cats, where our models offered 80% and 100% classification accuracy. We then used our models to assess non-identified data from awake monkeys and rabbits in order to highlight subsets of neurones with the greatest degree of similarity to identified cell classes. In this way, our GPC-based approach for tentatively identifying neurones from their spontaneous activity signatures, in the absence of an established ground-truth, nonetheless affords the experimenter a statistically robust means of grouping cells with properties matching known cell classes. Our approach therefore may have broad application to a variety of future cerebellar cortical investigations, particularly in awake animals where opportunities for definitive cell identification are limited.

Sensory representations in cerebellar granule cells.

Cerebellar granule cells are an attractive model system for examining synaptic transmission and temporal integration, because of their small number of excitatory synaptic inputs and electrotonic compactness. Recent in vivo whole-cell recordings have revealed how sensory stimuli are represented by synaptic activity across multiple modalities and cerebellar regions. By monitoring the activity of individual synapses, the reliability of these unitary signals has been quantified, and the complexity of a granule cell's receptive field has been explored at the highest resolution. Here we describe the emerging principles of synaptic sensory representation and their consequences for information processing in the granule cell layer.

Characterization of teleost Mdga1 using a gene-trap approach in medaka (Oryzias latipes).

MAM domain containing glycosilphosphatidilinositol anchor 1 (MDGA1) is an IgCAM protein present in many vertebrate species including humans. In mammals, MDGA1 is expressed by a subset of neurons in the developing brain and thought to function in neural cell migration. We identified a fish ortholog of mdga1 by a gene-trap screen utilizing the Frog Prince transposon in medaka (Japanese killifish, Oryzias latipes). The gene-trap vector was inserted into an intronic region of mdga1 to form a chimeric protein with green fluorescent protein, allowing us to monitor mdga1 expression in vivo. Expression of medaka mdga1 was seen in various types of embryonic brain neurons, and specifically in neurons migrating toward their target sites, supporting the proposed function of MDGA1. We also isolated the closely related mdga2 gene, whose expression partially overlapped with that of mdga1. Despite the fact that the gene-trap event eliminated most of the functional domains of the Mdga1 protein, homozygous embryos developed normally without any morphological abnormality, suggesting a functional redundancy of Mdga1 with other related proteins. High sequential homology of MDGA proteins between medaka and other vertebrate species suggests an essential role of the MDGA gene family in brain development among the vertebrate phylum.

The contribution of single synapses to sensory representation in vivo.

The extent to which synaptic activity can signal a sensory stimulus limits the information available to a neuron. We determined the contribution of individual synapses to sensory representation by recording excitatory postsynaptic currents (EPSCs) in cerebellar granule cells during a time-varying, quantifiable vestibular stimulus. Vestibular-sensitive synapses faithfully reported direction and velocity, rather than position or acceleration of whole-body motion, via bidirectional modulation of EPSC frequency. The lack of short-term synaptic dynamics ensured a highly linear relationship between velocity and charge transfer, and as few as 100 synapses provided resolution approaching psychophysical limits. This indicates that highly accurate stimulus representation can be achieved by small networks and even within single neurons.