Motion Detection Circuit Research Articles

A gate‐tunable memristor emulator for motion detection

SummaryWith its low power consumption and small size, the memristor has shown great potential for improving data storage density and computing efficiency. Compared to the dual‐port memristor, greater attention should be paid to researching gate‐tunable memristor for image processing to improve the processing speed and reduce hardware resource consumption. Developing gate‐tunable memristor emulators is highly attractive given the immaturity of current fabrication of the gate‐tunable memristor. This work proposes a digital gate‐tunable memristor emulator based on Raspberry Pi, which addresses the non‐reconfigurability and inflexibility issues of the analog emulators. The proposed emulator can match the behavior of different memristor devices by regulating the gate voltage parameter. Additionally, it can operate at a maximum frequency of 500 MHz. To test the functionality of the proposed emulator, a digital implementation of the memristive circuit for motion detection is designed and verified experimentally. Experiments demonstrate that when moving object detection is performed on a 640 × 350 pixel video stream, low power consumption of 53 mW and a delay of 3.52 μs can be achieved.

Basic analog–digital circuit for motion detection based on the vertebrate retina with low power consumption

Basic analog–digital circuit for motion detection based on the vertebrate retina with low power consumption

Brain wiring determinants uncovered by integrating connectomes and transcriptomes

Advances in brain connectomics have demonstrated the extraordinary complexity of neural circuits.1,2,3,4,5 Developing neurons encounter the axons and dendrites of many different neuron types and form synapses with only a subset of them. During circuit assembly, neurons express cell-type-specific repertoires comprising many cell adhesion molecules (CAMs) that can mediate interactions between developing neurites.6,7,8 Many CAM families have been shown to contribute to brain wiring in different ways.9,10 It has been challenging, however, to identify receptor-ligand pairs directly matching neurons with their synaptic targets. Here, we integrated the synapse-level connectome of the neural circuit11,12 with the developmental expression patterns7 and binding specificities of CAMs6,13 on pre- and postsynaptic neurons in the Drosophila visual system. To overcome the complexity of neural circuits, we focus on pairs of genetically related neurons that make differential wiring choices. In the motion detection circuit,14 closely related subtypes of T4/T5 neurons choose between alternative synaptic targets in adjacent layers of neuropil.12 This choice correlates with the matching expression in synaptic partners of different receptor-ligand pairs of the Beat and Side families of CAMs. Genetic analysis demonstrated that presynaptic Side-II and postsynaptic Beat-VI restrict synaptic partners to the same layer. Removal of this receptor-ligand pair disrupts layers and leads to inappropriate targeting of presynaptic sites and postsynaptic dendrites. We propose that different Side/Beat receptor-ligand pairs collaborate with other recognition molecules to determine wiring specificities in the fly brain. Combining transcriptomes, connectomes, and protein interactome maps allow unbiased identification of determinants of brain wiring.

Presynaptic depolarization differentially regulates dual neurotransmitter release from starburst amacrine cells in the mouse retina.

The retina is comprised of diverse neural networks, signaling from photoreceptors to ganglion cells to encode images. The synaptic connections between these retinal neurons are crucial points for information transfer; however, the input-output relations of many synapses are understudied. Starburst amacrine cells in the retina are known to contribute to retinal motion detection circuits, providing a unique window for understanding neural computations. We examined the dual transmitter release of GABA and acetylcholine from starburst amacrine cells by optogenetic activation of these cells, and conducted patch clamp recordings from postsynaptic ganglion cells to record excitatory and inhibitory postsynaptic currents (EPSCs and IPSCs). As starburst amacrine cells exhibit distinct kinetics in response to objects moving in a preferred or null direction, we mimicked their depolarization kinetics using optogenetic stimuli by varying slopes of the rising phase. The amplitudes of EPSCs and IPSCs in postsynaptic ganglion cells were reduced as the stimulus rising speed was prolonged. However, the sensitivity of postsynaptic currents to the stimulus slope differed. EPSC amplitudes were consistently reduced as the steepness of the rising phase fell. By contrast, IPSCs were less sensitive to the slope of the stimulus rise phase and maintained their amplitudes until the slope became shallow. These results indicate that distinct synaptic release mechanisms contribute to acetylcholine and GABA release from starburst amacrine cells, which could contribute to the ganglion cells' direction selectivity.

Excitatory and inhibitory neural dynamics jointly tune motion detection

Neurons integrate excitatory and inhibitory signals to produce their outputs, but the role of input timing in this integration remains poorly understood. Motion detection is a paradigmatic example of this integration, since theories of motion detection rely on different delays in visual signals. These delays allow circuits to compare scenes at different times to calculate the direction and speed of motion. Different motion detection circuits have different velocity sensitivity, but it remains untested how the response dynamics of individual cell types drive this tuning. Here, we sped up or slowed down specific neuron types in Drosophila's motion detection circuit by manipulating ion channel expression. Altering the dynamics of individual neuron types upstream of motion detectors increased their sensitivity to fast or slow visual motion, exposing distinct roles for excitatory and inhibitory dynamics in tuning directional signals, including a role for the amacrine cell CT1. A circuit model constrained by functional data and anatomy qualitatively reproduced the observed tuning changes. Overall, these results reveal how excitatory and inhibitory dynamics together tune a canonical circuit computation.

Ultrasensitive wearable sensor with novel hybrid structures of silver nanowires and carbon nanotubes in fluoroelastomer: Multi-directional sensing for human health monitoring and stretchable electronics

Today, wearable sensors are being developed for personalized human health monitoring for physical motion detection and physiological signal detection, and well-being. These wearable devices need to be extremely flexible, lightweight, and stretchable to be compatible with the contours of the human body, and mechanically and dynamically stable. Their functionality can be assured by simultaneously increasing conductivity, stretchability, and sensitivity for accurate and fast response. The challenge in fabricating stretchable sensors with these functionalities is embracing current wearable fashion. In this work, for the first time, a new 3D hybrid nanonetwork structures of high aspect ratio silver nanowires (AgNW) and carbon nanotubes (CNT) in fluoroelastomer FKM are developed. An optimized solution mixing (SM) and layer by layer (LBL) assembly technique, and LBL drop-coating process are employed to establish the hierarchical and shell-like nanostructures. The sensor films are ultra-thin and show high conductivity up to 2 × 105 S. m−1, high stretchability of up to 300%, and a maximum sensitivity (gage factor) of up to 2.5 × 107. The multifunctional sensor with hybrid nanonetwork shows high performance in various wearable electronic applications including human motion detection and stretchable light-emitting diode (LED) circuits. These ultra-sensitive materials show capability for multidirectional sensing by special configurations for each human body part movement, and small movements for human heartbeat monitoring.

Flexible filtering by neural inputs supports motion computation across states and stimuli

Sensory systems flexibly adapt their processing properties across a wide range of environmental and behavioral conditions. Such variable processing complicates attempts to extract a mechanistic understanding of sensory computations. This is evident in the highly constrained, canonical Drosophila motion detection circuit, where the core computation underlying direction selectivity is still debated despite extensive studies. Here we measured the filtering properties of neural inputs to the OFF motion-detecting T5 cell in Drosophila. We report state- and stimulus-dependent changes in the shape of these signals, which become more biphasic under specific conditions. Summing these inputs within the framework of a connectomic-constrained model of the circuit demonstrates that these shapes are sufficient to explain T5 responses to various motion stimuli. Thus, our stimulus- and state-dependent measurements reconcile motion computation with the anatomy of the circuit. These findings provide a clear example of how a basic circuit supports flexible sensory computation.

Neonicotinoid and sulfoximine pesticides differentially impair insect escape behavior and motion detection

Insect nervous systems offer unique advantages for studying interactions between sensory systems and behavior, given their complexity with high tractability. By examining the neural coding of salient environmental stimuli and resulting behavioral output in the context of environmental stressors, we gain an understanding of the effects of these stressors on brain and behavior and provide insight into normal function. The implication of neonicotinoid (neonic) pesticides in contributing to declines of nontarget species, such as bees, has motivated the development of new compounds that can potentially mitigate putative resistance in target species and declines of nontarget species. We used a neuroethologic approach, including behavioral assays and multineuronal recording techniques, to investigate effects of imidacloprid (IMD) and the novel insecticide sulfoxaflor (SFX) on visual motion-detection circuits and related escape behavior in the tractable locust system. Despite similar LD50 values, IMD and SFX evoked different behavioral and physiological effects. IMD significantly attenuated collision avoidance behaviors and impaired responses of neural populations, including decreases in spontaneous firing and neural habituation. In contrast, SFX displayed no effect at a comparable sublethal dose. These results show that neonics affect population responses and habituation of a visual motion detection system. We propose that differences in the sublethal effects of SFX reflect a different mode of action than that of IMD. More broadly, we suggest that neuroethologic assays for comparative neurotoxicology are valuable tools for fully addressing current issues regarding the proximal effects of environmental toxicity in nontarget species.

Dynamic Signal Compression for Robust Motion Vision in Flies

Sensory systems need to reliably extract information from highly variable natural signals. Flies, for instance, use optic flow to guide their course and are remarkably adept at estimating image velocity regardless of image statistics. Current circuit models, however, cannot account for this robustness. Here, we demonstrate that the Drosophila visual system reduces input variability by rapidly adjusting its sensitivity to local contrast conditions. We exhaustively map functional properties of neurons in the motion detection circuit and find that local responses are compressed by surround contrast. The compressive signal is fast, integrates spatially, and derives from neural feedback. Training convolutional neural networks on estimating the velocity of natural stimuli shows that this dynamic signal compression can close the performance gap between model and organism. Overall, our work represents a comprehensive mechanistic account of how neural systems attain the robustness to carry out survival-critical tasks in challenging real-world environments.

Sequential Nonlinear Filtering of Local Motion Cues by Global Motion Circuits

Many animals guide their movements using optic flow, the displacement of stationary objects across the retina caused by self-motion. How do animals selectively synthesize a global motion pattern from its local motion components? To what extent does this feature selectivity rely on circuit mechanisms versus dendritic processing? Here we used invivo calcium imaging to identify pre- and postsynaptic mechanisms for processing local motion signals in global motion detection circuits in Drosophila. Lobula plate tangential cells (LPTCs) detect global motion by pooling input from local motion detectors, T4/T5 neurons. We show that T4/T5 neurons suppress responses to adjacent local motion signals whereas LPTC dendrites selectively amplify spatiotemporal sequences of local motion signals consistent with preferred global patterns. We propose that sequential nonlinear suppression and amplification operations allow optic flow circuitry to simultaneously prevent saturating responses to local signals while creating selectivity for global motion patterns critical to behavior.

Drosophila Sidekick is required in developing photoreceptors to enable visual motion detection.

The assembly of functional neuronal circuits requires growth cones to extend in defined directions and recognize the correct synaptic partners. Homophilic adhesion between vertebrate Sidekick proteins promotes synapse formation between retinal neurons involved in visual motion detection. We show here that Drosophila Sidekick accumulates in specific synaptic layers of the developing motion detection circuit and is necessary for normal optomotor behavior. Sidekick is required in photoreceptors, but not in their target lamina neurons, to promote the alignment of lamina neurons into columns and subsequent sorting of photoreceptor axons into synaptic modules based on their precise spatial orientation. Sidekick is also localized to the dendrites of the direction-selective T4 and T5 cells, and is expressed in some of their presynaptic partners. In contrast to its vertebrate homologs, Sidekick is not essential for T4 and T5 to direct their dendrites to the appropriate layers or to receive synaptic contacts. These results illustrate a conserved requirement for Sidekick proteins in establishing visual motion detection circuits that is achieved through distinct cellular mechanisms in Drosophila and vertebrates.

Simple analog-digital motion detection circuit and its application to mobile robot

Simple analog-digital circuits for detecting the motion direction based on the information processing of the vertebrate retina were proposed. A network constructed with an array of proposed circuits was applied to a mobile robot. A test circuit was fabricated using discrete metal oxide semiconductor (MOS) transistors on a breadboard. The measured results of the test circuit showed that the unit circuit can output a motion signal. The motion sensor for detecting the movement direction, which consists of an array of unit circuits, was connected to the microcomputer introduced in the mobile robot. It was clarified that the proposed circuits can control the mobile robot.

Wide-field feedback neurons dynamically tune early visual processing.

An important strategy for efficient neural coding is to match the range of cellular responses to the distribution of relevant input signals. However, the structure and relevance of sensory signals depend on behavioral state. Here, we show that behavior modifies neural activity at the earliest stages of fly vision. We describe a class of wide-field neurons that provide feedback to the most peripheral layer of the Drosophila visual system, the lamina. Using in vivo patch-clamp electrophysiology, we found that lamina wide-field neurons respond to low-frequency luminance fluctuations. Recordings in flying flies revealed that the gain and frequency tuning of wide-field neurons change during flight, and that these effects are mimicked by the neuromodulator octopamine. Genetically silencing wide-field neurons increased behavioral responses to slow-motion stimuli. Together, these findings identify a cell type that is gated by behavior to enhance neural coding by subtracting low-frequency signals from the inputs to motion detection circuits.

Structured Connectivity in Cerebellar Inhibitory Networks

SummaryDefining the rules governing synaptic connectivity is key to formulating theories of neural circuit function. Interneurons can be connected by both electrical and chemical synapses, but the organization and interaction of these two complementary microcircuits is unknown. By recording from multiple molecular layer interneurons in the cerebellar cortex, we reveal specific, nonrandom connectivity patterns in both GABAergic chemical and electrical interneuron networks. Both networks contain clustered motifs and show specific overlap between them. Chemical connections exhibit a preference for transitive patterns, such as feedforward triplet motifs. This structured connectivity is supported by a characteristic spatial organization: transitivity of chemical connectivity is directed vertically in the sagittal plane, and electrical synapses appear strictly confined to the sagittal plane. The specific, highly structured connectivity rules suggest that these motifs are essential for the function of the cerebellar network.

A visual motion detection circuit suggested by Drosophila connectomics.

SummaryAnimal behavior 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 developed 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 medulla's repeating module. 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.

Accurate maps of visual circuitry

Such is the brain's complexity that even small neural circuits contain hundreds of neurons making thousands of connections. Connectivity and optical analyses provide close-up views of two such circuits. See Articles p.168 , p.175 & Letter p.212 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.

Behaviorally Related Neural Plasticity in the Arthropod Optic Lobes

Due to the complexity and variability of natural environments, the ability to adaptively modify behavior is of fundamental biological importance. Motion vision provides essential cues for guiding critical behaviors such as prey, predator, or mate detection. However, when confronted with the repeated sight of a moving object that turns out to be irrelevant, most animals will learn to ignore it. The neural mechanisms by which moving objects can be ignored are unknown. Although many arthropods exhibit behavioral adaptation to repetitive moving objects, the underlying neural mechanisms have been difficult to study, due to the difficulty of recording activity from the small columnar neurons in peripheral motion detection circuits. We developed an experimental approach in an arthropod to record the calcium responses of visual neurons invivo. We show that peripheral columnar neurons that convey visual information into the second optic neuropil persist in responding to the repeated presentation of an innocuous moving object. However, activity in the columnar neurons that convey the visual information from the second to the third optic neuropil is suppressed during high-frequency stimulus repetitions. In accordance with the animal's behavioral changes, the suppression of neural activity is fast but short lasting and restricted to the retina's trained area. Columnar neurons from the second optic neuropil are likely the main plastic locus responsible for the modifications in animal behavior when confronted with rapidly repeated object motion. Our results demonstrate that visually guided behaviors can be determined by neural plasticity that occurs surprisingly early in the visual pathway.

Versatile SQUID Susceptometer With Multiple Measurement Modes

We present a state of the art SQUID-based susceptometer which supports lock-in style vibrating sample magnetometer (VSM) measurements as well as traditional dc scans using a single motion control and detection circuit for both measurements. The dc scan fits the measured SQUID response as function of position to an expected response waveform and includes algorithm enhancements for better rejection of external magnetic signals. The VSM-style measurement uses a novel approach for its data acquisition, which is designed to minimize cross talk between the mechanical movement of the sample and the measured signal. In addition, the superconducting gradiometer of the detection circuit is optimized for best signal-to-noise performance in both types of measurements, achieving sensitivity in the range of 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-8</sup> emu at fields up to 7 Tesla. This dual measurement capability allows a single instrument to measure a wide variety of samples by choosing the method most fitting to constraints imposed by the sample, e.g., limiting thermal and magnetic field variations or reducing forces the sample is exposed to during the measurement. Special consideration is given to a detailed analysis of the advantages of the different measurement methods, the influence of measurement parameters on the reported results, as well as the effect of sample properties and sample geometry on the reported moment.

Functional Specialization of Parallel Motion Detection Circuits in the Fly

In the fly Drosophila melanogaster, photoreceptor input to motion vision is split into two parallel pathways as represented by first-order interneurons L1 and L2 (Rister et al., 2007; Joesch et al., 2010). However, how these pathways are functionally specialized remains controversial. One study (Eichner et al., 2011) proposed that the L1-pathway evaluates only sequences of brightness increments (ON-ON), while the L2-pathway processes exclusively brightness decrements (OFF-OFF). Another study (Clark et al., 2011) proposed that each of the two pathways evaluates both ON-ON and OFF-OFF sequences. To decide between these alternatives, we recorded from motion-sensitive neurons in flies in which the output from either L1 or L2 was genetically blocked. We found that blocking L1 abolishes ON-ON responses but leaves OFF-OFF responses intact. The opposite was true, when the output from L2 was blocked. We conclude that the L1 and L2 pathways are functionally specialized to detect ON-ON and OFF-OFF sequences, respectively.

2A1-J02 網膜に基づく動き検出電子回路の構築(ロボットビジョン(1))

2A1-J02 網膜に基づく動き検出電子回路の構築(ロボットビジョン(1))