Spike-frequency adaptation and intrinsic properties of an identified, looming-sensitive neuron

A Biomimetic Visual Detection Model: Event-Driven LGMDs Implemented With Fractional Spiking Neuron Circuits

OBJECTIVE

Lobula giant motion detectors (LGMDs) in locusts effectively predict collisions and trigger avoidance, with potential applications in autonomous driving and UAVs. Research on LGMD characteristics splits into two views: one focusing on the presynaptic visual pathway, the other on the postsynaptic LGMD neurons. Both perspectives have support, leading to two computational models, but they lack a biophysical description of the individual LGMD neuron behavior. This paper aims to mimic and explain LGMD behavior based on fractional spiking neurons (FSNs) and construct a biomimetic visual model for the LGMD compatible with these characteristics.

METHODS

We implement the visual model using an event camera to simulate photoreceptors and follow the ON/OFF visual pathway, incorporating lateral inhibition to mimic the LGMD system from the bottom up. Second, most computational models of motion perception use only the dendrites within the LGMD neurons as the ideal pathway for linear summation, ignoring dendritic effects inducing neuronal properties. Thus, we introduce FSN circuits by altering dendritic morphological parameters to simulate multi-scale spike frequency adaptation (SFA) observed in LGMDs. Additionally, we add one more circuit of dendritic trees into the FSNs to be compatible with the postsynaptic feed-forward inhibition (FFI) in LGMD neurons, providing a novel explanatory and predictive model.

RESULTS

We test that the event-driven biomimetic visual model can achieve collision detection and looming selection in different complex scenes, especially fast-moving objects.

A Robust Visual System for Looming Cue Detection Against Translating Motion

Collision detection is critical for autonomous vehicles or robots to serve human society safely. Detecting looming objects robustly and timely plays an important role in collision avoidance systems. The locust lobula giant movement detector (LGMD1) is specifically selective to looming objects which are on a direct collision course. However, the existing LGMD1 models cannot distinguish a looming object from a near and fast translatory moving object, because the latter can evoke a large amount of excitation that can lead to false LGMD1 spikes. This article presents a new visual neural system model (LGMD1) that applies a neural competition mechanism within a framework of separated ON and OFF pathways to shut off the translating response. The competition-based approach responds vigorously to monotonous ON/OFF responses resulting from a looming object. However, it does not respond to paired ON-OFF responses that result from a translating object, thereby enhancing collision selectivity. Moreover, a complementary denoising mechanism ensures reliable collision detection. To verify the effectiveness of the model, we have conducted systematic comparative experiments on synthetic and real datasets. The results show that our method exhibits more accurate discrimination between looming and translational events-the looming motion can be correctly detected. It also demonstrates that the proposed model is more robust than comparative models.

Enhancing LGMD-based model for collision prediction via binocular structure

Introduction

Lobular giant motion detector (LGMD) neurons, renowned for their distinctive response to looming stimuli, inspire the development of visual neural network models for collision prediction. However, the existing LGMD-based models could not yet incorporate the invaluable feature of depth distance and still suffer from the following two primary drawbacks. Firstly, they struggle to effectively distinguish the three fundamental motion patterns of approaching, receding, and translating, in contrast to the natural abilities of LGMD neurons. Secondly, due to their reliance on a general determination process employing an activation function and fixed threshold for output, these models exhibit dramatic fluctuations in prediction effectiveness across different scenarios.

Methods

To address these issues, we propose a novel LGMD-based model with a binocular structure (Bi-LGMD). The depth distance of the moving object is extracted by calculating the binocular disparity facilitating a clear differentiation of the motion patterns, after obtaining the moving object's contour through the basic components of the LGMD network. In addition, we introduce a self-adaptive warning depth-distance, enhancing the model's robustness in various motion scenarios.

Results

The effectiveness of the proposed model is verified using computer-simulated and real-world videos.

Discussion

Furthermore, the experimental results demonstrate that the proposed model is robust to contrast and noise.

Comparative Effects of Domain-Specific Human Monoclonal Antibodies Against LGI1 on Neuronal Excitability

BACKGROUND AND OBJECTIVES

Autoantibodies to leucine-rich glioma inactivated protein 1 (LGI1) cause an autoimmune limbic encephalitis with frequent focal seizures and anterograde memory dysfunction. LGI1 is a neuronal secreted linker protein with 2 functional domains: the leucine-rich repeat (LRR) and epitempin (EPTP) regions. LGI1 autoantibodies are known to interfere with presynaptic function and neuronal excitability; however, their epitope-specific mechanisms are incompletely understood.

METHODS

We used patient-derived monoclonal autoantibodies (mAbs), which target either LRR or EPTP domains of LGI1 to investigate long-term antibody-induced alteration of neuronal function. LRR- and EPTP-specific effects were evaluated by patch-clamp recordings in cultured hippocampal neurons and compared with biophysical neuron modeling. Kv1.1 channel clustering at the axon initial segment (AIS) was quantified by immunocytochemistry and structured illumination microscopy techniques.

RESULTS

Both EPTP and LRR domain-specific mAbs decreased the latency of first somatic action potential firing. However, only the LRR-specific mAbs increased the number of action potential firing together with enhanced initial instantaneous frequency and promoted spike-frequency adaptation, which were less pronounced after the EPTP mAb. This also led to an effective reduction in the slope of ramp-like depolarization in the subthreshold response, suggesting Kv1 channel dysfunction. A biophysical model of a hippocampal neuron corroborated experimental results and suggests that an isolated reduction of the conductance of Kv1-mediated K+ currents largely accounts for the antibody-induced alterations in the initial firing phase and spike-frequency adaptation. Furthermore, Kv1.1 channel density was spatially redistributed from the distal toward the proximal site of AIS under LRR mAb treatment and, to a lesser extant, under EPTP mAb.

DISCUSSION

These findings indicate an epitope-specific pathophysiology of LGI1 autoantibodies. The pronounced neuronal hyperexcitability and SFA together with dropped slope of ramp-like depolarization after LRR-targeted interference suggest disruption of LGI1-dependent clustering of K+ channel complexes. Moreover, considering the effective triggering of action potentials at the distal AIS, the altered spatial distribution of Kv1.1 channel density may contribute to these effects through impairing neuronal control of action potential initiation and synaptic integration.

Bioinspired figure-ground discrimination via visual motion smoothing

Flies detect and track moving targets among visual clutter, and this process mainly relies on visual motion. Visual motion is analyzed or computed with the pathway from the retina to T4/T5 cells. The computation of local directional motion was formulated as an elementary movement detector (EMD) model more than half a century ago. Solving target detection or figure-ground discrimination problems can be equivalent to extracting boundaries between a target and the background based on the motion discontinuities in the output of a retinotopic array of EMDs. Individual EMDs cannot measure true velocities, however, due to their sensitivity to pattern properties such as luminance contrast and spatial frequency content. It remains unclear how local directional motion signals are further integrated to enable figure-ground discrimination. Here, we present a computational model inspired by fly motion vision. Simulations suggest that the heavily fluctuating output of an EMD array is naturally surmounted by a lobula network, which is hypothesized to be downstream of the local motion detectors and have parallel pathways with distinct directional selectivity. The lobula network carries out a spatiotemporal smoothing operation for visual motion, especially across time, enabling the segmentation of moving figures from the background. The model qualitatively reproduces experimental observations in the visually evoked response characteristics of one type of lobula columnar (LC) cell. The model is further shown to be robust to natural scene variability. Our results suggest that the lobula is involved in local motion-based target detection.

Amyloid Beta Alters Prefrontal-dependent Functions Along with its Excitability and Synaptic Plasticity in Male Rats

Prefrontal cortex (PFC)-related functions, such as working memory (WM) and cognitive flexibility (CF), are among the first to be altered at early stages of Alzheimer's disease (AD). Likewise, transgenic AD models carrying different AD-related mutations, mostly linked to the overproduction of amyloid beta (Aβ) and other peptides, show premature behavioral and functional symptoms associated with PFC alterations. However, little is known about the effects of intracerebral or intra-PFC Aβ infusion on WM and CF, as well as on pyramidal cell excitability and plasticity. Thus, here we evaluated the effects of a single Aβ injection, directly into the PFC, or its intracerebroventricular (icv) application, on PFC-dependent behaviors and on the intrinsic and synaptic properties of layer V pyramidal neurons in PFC slices. We found that a single icv Aβ infusion reduced learning and performance of a delayed non-matching-to-sample WM task and prevented reversal learning in a matching-to-sample version of the task, several weeks after its infusion. The inhibition of WM performance was reproduced more potently by a single PFC Aβ infusion and was associated with Aβ accumulation. This behavioral disruption was related to increased layer V pyramidal cell firing, larger sag membrane potential, increased fast after-hyperpolarization and a failure to sustain synaptic long-term potentiation, even leading to long-term depression, at both the hippocampal-PFC pathway and intracortical synapses. These findings show that Aβ can affect PFC excitability and synaptic plasticity balance, damaging PFC-dependent functions, which could constitute the foundations of the early alterations in executive functions in AD patients.

A Looming Spatial Localization Neural Network Inspired by MLG1 Neurons in the Crab Neohelice

Similar to most visual animals, the crab Neohelice granulata relies predominantly on visual information to escape from predators, to track prey and for selecting mates. It, therefore, needs specialized neurons to process visual information and determine the spatial location of looming objects. In the crab Neohelice granulata, the Monostratified Lobula Giant type1 (MLG1) neurons have been found to manifest looming sensitivity with finely tuned capabilities of encoding spatial location information. MLG1s neuronal ensemble can not only perceive the location of a looming stimulus, but are also thought to be able to influence the direction of movement continuously, for example, escaping from a threatening, looming target in relation to its position. Such specific characteristics make the MLG1s unique compared to normal looming detection neurons in invertebrates which can not localize spatial looming. Modeling the MLG1s ensemble is not only critical for elucidating the mechanisms underlying the functionality of such neural circuits, but also important for developing new autonomous, efficient, directionally reactive collision avoidance systems for robots and vehicles. However, little computational modeling has been done for implementing looming spatial localization analogous to the specific functionality of MLG1s ensemble. To bridge this gap, we propose a model of MLG1s and their pre-synaptic visual neural network to detect the spatial location of looming objects. The model consists of 16 homogeneous sectors arranged in a circular field inspired by the natural arrangement of 16 MLG1s' receptive fields to encode and convey spatial information concerning looming objects with dynamic expanding edges in different locations of the visual field. Responses of the proposed model to systematic real-world visual stimuli match many of the biological characteristics of MLG1 neurons. The systematic experiments demonstrate that our proposed MLG1s model works effectively and robustly to perceive and localize looming information, which could be a promising candidate for intelligent machines interacting within dynamic environments free of collision. This study also sheds light upon a new type of neuromorphic visual sensor strategy that can extract looming objects with locational information in a quick and reliable manner.

Sex Differences in Biophysical Signatures across Molecularly Defined Medial Amygdala Neuronal Subpopulations

The medial amygdala (MeA) is essential for processing innate social and non-social behaviors, such as territorial aggression and mating, which display in a sex-specific manner. While sex differences in cell numbers and neuronal morphology in the MeA are well established, if and how these differences extend to the biophysical level remain unknown. Our previous studies revealed that expression of the transcription factors, Dbx1 and Foxp2, during embryogenesis defines separate progenitor pools destined to generate different subclasses of MEA inhibitory output neurons. We have also previously shown that Dbx1-lineage and Foxp2-lineage neurons display different responses to innate olfactory cues and in a sex-specific manner. To examine whether these neurons also possess sex-specific biophysical signatures, we conducted a multidimensional analysis of the intrinsic electrophysiological profiles of these transcription factor defined neurons in the male and female MeA. We observed striking differences in the action potential (AP) spiking patterns across lineages, and across sex within each lineage, properties known to be modified by different voltage-gated ion channels. To identify the potential mechanism underlying the observed lineage-specific and sex-specific differences in spiking adaptation, we conducted a phase plot analysis to narrow down putative ion channel candidates. Of these candidates, we found a subset expressed in a lineage-biased and/or sex-biased manner. Thus, our results uncover neuronal subpopulation and sex differences in the biophysical signatures of developmentally defined MeA output neurons, providing a potential physiological substrate for how the male and female MeA may process social and non-social cues that trigger innate behavioral responses.

Active membrane conductances and morphology of a collision detection neuron broaden its impedance profile and improve discrimination of input synchrony

How neurons filter and integrate their complex patterns of synaptic inputs is central to their role in neural information processing. Synaptic filtering and integration are shaped by the frequency-dependent neuronal membrane impedance. Using single and dual dendritic recordings in vivo, pharmacology, and computational modeling, we characterized the membrane impedance of a collision detection neuron in the grasshopper Schistocerca americana. This neuron, the lobula giant movement detector (LGMD), exhibits consistent impedance properties across frequencies and membrane potentials. Two common active conductances g_H and g_M, mediated respectively by hyperpolarization-activated cyclic nucleotide-gated (HCN) channels and by muscarine-sensitive M-type K⁺ channels, promote broadband integration with high temporal precision over the LGMD's natural range of membrane potentials and synaptic input frequencies. Additionally, we found that a model based on the LGMD's branching morphology increased the gain and decreased the delay associated with the mapping of synaptic input currents to membrane potential. More generally, this was true for a wide range of model neuron morphologies, including those of neocortical pyramidal neurons and cerebellar Purkinje cells. These findings show the unexpected role played by two widespread active conductances and by dendritic morphology in shaping synaptic integration.NEW & NOTEWORTHY Neuronal filtering and integration of synaptic input patterns depend on the electrochemical properties of dendrites. We used an identified collision detection neuron in grasshoppers to examine how its morphology and two conductances affect its membrane impedance in relation to the computations it performs. The neuronal properties examined are ubiquitous and therefore promote a general understanding of neuronal computations, including those in the human brain.

Towards Computational Models and Applications of Insect Visual Systems for Motion Perception: A Review

Motion perception is a critical capability determining a variety of aspects of insects' life, including avoiding predators, foraging, and so forth. A good number of motion detectors have been identified in the insects' visual pathways. Computational modeling of these motion detectors has not only been providing effective solutions to artificial intelligence, but also benefiting the understanding of complicated biological visual systems. These biological mechanisms through millions of years of evolutionary development will have formed solid modules for constructing dynamic vision systems for future intelligent machines. This article reviews the computational motion perception models originating from biological research on insects' visual systems in the literature. These motion perception models or neural networks consist of the looming-sensitive neuronal models of lobula giant movement detectors (LGMDs) in locusts, the translation-sensitive neural systems of direction-selective neurons (DSNs) in fruit flies, bees, and locusts, and the small-target motion detectors (STMDs) in dragonflies and hoverflies. We also review the applications of these models to robots and vehicles. Through these modeling studies, we summarize the methodologies that generate different direction and size selectivity in motion perception. Finally, we discuss multiple systems integration and hardware realization of these bio-inspired motion perception models.

Shaping the collision selectivity in a looming sensitive neuron model with parallel ON and OFF pathways and spike frequency adaptation

Shaping the collision selectivity in vision-based artificial collision-detecting systems is still an open challenge. This paper presents a novel neuron model of a locust looming detector, i.e. the lobula giant movement detector (LGMD1), in order to provide effective solutions to enhance the collision selectivity of looming objects over other visual challenges. We propose an approach to model the biologically plausible mechanisms of ON and OFF pathways and a biophysical mechanism of spike frequency adaptation (SFA) in the proposed LGMD1 visual neural network. The ON and OFF pathways can separate both dark and light looming features for parallel spatiotemporal computations. This works effectively on perceiving a potential collision from dark or light objects that approach; such a bio-plausible structure can also separate LGMD1's collision selectivity to its neighbouring looming detector - the LGMD2. The SFA mechanism can enhance the LGMD1's collision selectivity to approaching objects rather than receding and translating stimuli, which is a significant improvement compared with similar LGMD1 neuron models. The proposed framework has been tested using off-line tests of synthetic and real-world stimuli, as well as on-line bio-robotic tests. The enhanced collision selectivity of the proposed model has been validated in systematic experiments. The computational simplicity and robustness of this work have also been verified by the bio-robotic tests, which demonstrates potential in building neuromorphic sensors for collision detection in both a fast and reliable manner.

M current regulates firing mode and spike reliability in a collision-detecting neuron

All animals must detect impending collisions to escape and reliably discriminate them from nonthreatening stimuli, thus preventing false alarms. Therefore, it is no surprise that animals have evolved highly selective and sensitive neurons dedicated to such tasks. We examined a well-studied collision-detection neuron in the grasshopper ( Schistocerca americana) using in vivo electrophysiology, pharmacology, and computational modeling. This lobula giant movement detector (LGMD) neuron is excitable by inputs originating from each ommatidia of the compound eye. It possesses many intrinsic properties that increase its selectivity to objects approaching on a collision course, including switching between burst and nonburst firing. In this study, we demonstrate that the LGMD neuron exhibits a large M current, generated by noninactivating K⁺ channels, that shortens the temporal window of dendritic integration, regulates a firing mode switch between burst and isolated spiking, increases the precision of spike timing, and increases the reliability of spike propagation to downstream motor centers. By revealing how the M current increases the LGMD's ability to detect impending collisions, our results suggest that similar channels may play an analogous role in other collision detection circuits. NEW & NOTEWORTHY The ability to reliably detect impending collisions is a critical survival skill. The nervous systems of many animals have developed dedicated neurons for accomplishing this task. We used a mix of in vivo electrophysiology and computational modeling to investigate the role of M potassium channels within one such collision-detecting neuron and show that through regulation of burst firing and enhancement of spiking reliability, the M current increases the ability to detect impending collisions.

Feedforward Inhibition Conveys Time-Varying Stimulus Information in a Collision Detection Circuit

Feedforward inhibition is ubiquitous as a motif in the organization of neuronal circuits. During sensory information processing, it is traditionally thought to sharpen the responses and temporal tuning of feedforward excitation onto principal neurons. As it often exhibits complex time-varying activation properties, feedforward inhibition could also convey information used by single neurons to implement dendritic computations on sensory stimulus variables. We investigated this possibility in a collision-detecting neuron of the locust optic lobe that receives both feedforward excitation and inhibition. We identified a small population of neurons mediating feedforward inhibition, with wide visual receptive fields and whose responses depend both on the size and speed of moving stimuli. By studying responses to simulated objects approaching on a collision course, we determined that they jointly encode the angular size of expansion of the stimulus. Feedforward excitation, on the other hand, encodes a function of the angular velocity of expansion and the targeted collision-detecting neuron combines these two variables non-linearly in its firing output. Thus, feedforward inhibition actively contributes to the detailed firing-rate time course of this collision-detecting neuron, a feature critical to the appropriate execution of escape behaviors. These results suggest that feedforward inhibition could similarly convey time-varying stimulus information in other neuronal circuits.

Biophysics of object segmentation in a collision-detecting neuron

Collision avoidance is critical for survival, including in humans, and many species possess visual neurons exquisitely sensitive to objects approaching on a collision course. Here, we demonstrate that a collision-detecting neuron can detect the spatial coherence of a simulated impending object, thereby carrying out a computation akin to object segmentation critical for proper escape behavior. At the cellular level, object segmentation relies on a precise selection of the spatiotemporal pattern of synaptic inputs by dendritic membrane potential-activated channels. One channel type linked to dendritic computations in many neural systems, the hyperpolarization-activated cation channel, HCN, plays a central role in this computation. Pharmacological block of HCN channels abolishes the neuron's spatial selectivity and impairs the generation of visually guided escape behaviors, making it directly relevant to survival. Additionally, our results suggest that the interaction of HCN and inactivating K⁺ channels within active dendrites produces neuronal and behavioral object specificity by discriminating between complex spatiotemporal synaptic activation patterns.

What has been missed for predicting human attention in viewing driving clips?

Recent research progress on the topic of human visual attention allocation in scene perception and its simulation is based mainly on studies with static images. However, natural vision requires us to extract visual information that constantly changes due to egocentric movements or dynamics of the world. It is unclear to what extent spatio-temporal regularity, an inherent regularity in dynamic vision, affects human gaze distribution and saliency computation in visual attention models. In this free-viewing eye-tracking study we manipulated the spatio-temporal regularity of traffic videos by presenting them in normal video sequence, reversed video sequence, normal frame sequence, and randomised frame sequence. The recorded human gaze allocation was then used as the 'ground truth' to examine the predictive ability of a number of state-of-the-art visual attention models. The analysis revealed high inter-observer agreement across individual human observers, but all the tested attention models performed significantly worse than humans. The inferior predictability of the models was evident from indistinguishable gaze prediction irrespective of stimuli presentation sequence, and weak central fixation bias. Our findings suggest that a realistic visual attention model for the processing of dynamic scenes should incorporate human visual sensitivity with spatio-temporal regularity and central fixation bias.

Aging-related impairments of hippocampal mossy fibers synapses on CA3 pyramidal cells

The network interaction between the dentate gyrus and area CA3 of the hippocampus is responsible for pattern separation, a process that underlies the formation of new memories, and which is naturally diminished in the aged brain. At the cellular level, aging is accompanied by a progression of biochemical modifications that ultimately affects its ability to generate and consolidate long-term potentiation. Although the synapse between dentate gyrus via the mossy fibers (MFs) onto CA3 neurons has been subject of extensive studies, the question of how aging affects the MF-CA3 synapse is still unsolved. Extracellular and whole-cell recordings from acute hippocampal slices of aged Wistar rats (34 ± 2 months old) show that aging is accompanied by a reduction in the interneuron-mediated inhibitory mechanisms of area CA3. Several MF-mediated forms of short-term plasticity, MF long-term potentiation and at least one of the critical signaling cascades necessary for potentiation are also compromised in the aged brain. An analysis of the spontaneous glutamatergic and gamma-aminobutyric acid-mediated currents on CA3 cells reveal a dramatic alteration in amplitude and frequency of the nonevoked events. CA3 cells also exhibited increased intrinsic excitability. Together, these results demonstrate that aging is accompanied by a decrease in the GABAergic inhibition, reduced expression of short- and long-term forms of synaptic plasticity, and increased intrinsic excitability.

Burst Firing in a Motion-Sensitive Neural Pathway Correlates with Expansion Properties of Looming Objects that Evoke Avoidance Behaviors

The locust visual system contains a well-defined motion-sensitive pathway that transfers visual input to motor centers involved in predator evasion and collision avoidance. One interneuron in this pathway, the descending contralateral movement detector (DCMD), is typically described as using rate coding; edge expansion of approaching objects causes an increased rate of neuronal firing that peaks after a certain retinal threshold angle is exceeded. However, evidence of intrinsic DCMD bursting properties combined with observable oscillations in mean firing rates and tight clustering of spikes in raw traces, suggest that bursting may be important for motion detection. Sensory neuron bursting provides important timing information about dynamic stimuli in many model systems, yet no studies have rigorously investigated if bursting occurs in the locust DCMD during object approach. We presented repetitions of 30 looming stimuli known to generate behavioral responses to each of 20 locusts in order to identify and quantify putative bursting activity in the DCMD. Overall, we found a bimodal distribution of inter-spike intervals (ISI) with peaks of more frequent and shorter ISIs occurring from 1–8 ms and longer less frequent ISIs occurring from 40–50 ms. Subsequent analysis identified bursts and isolated single spikes from the responses. Bursting frequency increased in the latter phase of an approach and peaked at the time of collision, while isolated spiking was predominant during the beginning of stimulus approach. We also found that the majority of inter-burst intervals (IBIs) occurred at 40–50 ms (or 20–25 bursts/s). Bursting also occurred across varied stimulus parameters and suggests that burst timing may be a key component of looming detection. Our findings suggest that the DCMD uses two modes of coding to transmit information about looming stimuli and that these modes change dynamically with a changing stimulus at a behaviorally-relevant time.

Patterns of interval correlations in neural oscillators with adaptation

Neural firing is often subject to negative feedback by adaptation currents. These currents can induce strong correlations among the time intervals between spikes. Here we study analytically the interval correlations of a broad class of noisy neural oscillators with spike-triggered adaptation of arbitrary strength and time scale. Our weak-noise theory provides a general relation between the correlations and the phase-response curve (PRC) of the oscillator, proves anti-correlations between neighboring intervals for adapting neurons with type I PRC and identifies a single order parameter that determines the qualitative pattern of correlations. Monotonically decaying or oscillating correlation structures can be related to qualitatively different voltage traces after spiking, which can be explained by the phase plane geometry. At high firing rates, the long-term variability of the spike train associated with the cumulative interval correlations becomes small, independent of model details. Our results are verified by comparison with stochastic simulations of the exponential, leaky, and generalized integrate-and-fire models with adaptation.

Channel noise from both slow adaptation currents and fast currents is required to explain spike-response variability in a sensory neuron

Spike-timing variability has a large effect on neural information processing. However, for many systems little is known about the noise sources causing the spike-response variability. Here we investigate potential sources of spike-response variability in auditory receptor neurons of locusts, a classic insect model system. At low-spike frequencies, our data show negative interspike-interval (ISI) correlations and ISI distributions that match the inverse Gaussian distribution. These findings can be explained by a white-noise source that interacts with an adaptation current. At higher spike frequencies, more strongly peaked distributions and positive ISI correlations appear, as expected from a canonical model of suprathreshold firing driven by temporally correlated (i.e., colored) noise. Simulations of a minimal conductance-based model of the auditory receptor neuron with stochastic ion channels exclude the delayed rectifier as a possible noise source. Our analysis suggests channel noise from an adaptation current and the receptor or sodium current as main sources for the colored and white noise, respectively. By comparing the ISI statistics with generic models, we find strong evidence for two distinct noise sources. Our approach does not involve any dendritic or somatic recordings that may harm the delicate workings of many sensory systems. It could be applied to various other types of neurons, in which channel noise dominates the fluctuations that shape the neuron's spike statistics.

A looming-sensitive pathway responds to changes in the trajectory of object motion

Two identified locust neurons, the lobula giant movement detector (LGMD) and its postsynaptic partner, the descending contralateral movement detector (DCMD), constitute one motion-sensitive pathway in the visual system that responds preferentially to objects that approach on a direct collision course and are implicated in collision-avoidance behavior. Previously described responses to the approach of paired objects and approaches at different time intervals (Guest BB, Gray JR. J Neurophysiol 95: 1428–1441, 2006) suggest that this pathway may also be affected by more complicated movements in the locust's visual environment. To test this possibility we presented stationary locusts with disks traveling along combinations of colliding (looming), noncolliding (translatory), and near-miss trajectories. Distinctly different responses to different trajectories and trajectory changes demonstrate that DCMD responds to complex aspects of local visual motion. DCMD peak firing rates associated with the time of collision remained relatively invariant after a trajectory change from translation to looming. Translatory motion initiated in the frontal visual field generated a larger peak firing rate relative to object motion initiated in the posterior visual field, and the peak varied with simulated distance from the eye. Transition from translation to looming produced a transient decrease in the firing rate, whereas transition away from looming produced a transient increase. The change in firing rate at the time of transition was strongly correlated with unique expansion parameters described by the instantaneous angular acceleration of the leading edge and subtense angle of the disk. However, response time remained invariant. While these results may reflect low spatial resolution of the compound eye, they also suggest that this motion-sensitive pathway may be capable of monitoring dynamic expansion properties of objects that change the trajectory of motion.

Impact of neural noise on a sensory-motor pathway signaling impending collision

Noise is a major concern in circuits processing electrical signals, including neural circuits. There are many factors that influence how noise propagates through neural circuits, and there are few systems in which noise levels have been studied throughout a processing pathway. We recorded intracellularly from multiple stages of a sensory-motor pathway in the locust that detects approaching objects. We found that responses are more variable and that signal-to-noise ratios (SNRs) are lower further from the sensory periphery. SNRs remain low even with the use of stimuli for which the pathway is most selective and for which the neuron representing its final sensory level must integrate many synaptic inputs. Modeling of this neuron shows that variability in the strength of individual synaptic inputs within a large population has little effect on the variability of the spiking output. In contrast, jitter in the timing of individual inputs and spontaneous variability is important for shaping the responses to preferred stimuli. These results suggest that neural noise is inherent to the processing of visual stimuli signaling impending collision and contributes to shaping neural responses along this sensory-motor pathway.

Collision detection as a model for sensory-motor integration

Visually guided collision avoidance is critical for the survival of many animals. The execution of successful collision-avoidance behaviors requires accurate processing of approaching threats by the visual system and signaling of threat characteristics to motor circuits to execute appropriate motor programs in a timely manner. Consequently, visually guided collision avoidance offers an excellent model with which to study the neural mechanisms of sensory-motor integration in the context of a natural behavior. Neurons that selectively respond to approaching threats and brain areas processing them have been characterized across many species. In locusts in particular, the underlying sensory and motor processes have been analyzed in great detail: These animals possess an identified neuron, called the LGMD, that responds selectively to approaching threats and conveys that information through a second identified neuron, the DCMD, to motor centers, generating escape jumps. A combination of behavioral and in vivo electrophysiological experiments has unraveled many of the cellular and network mechanisms underlying this behavior.

Neural adaptation facilitates oscillatory responses to static inputs in a recurrent network of ON and OFF cells

We investigate the role of adaptation in a neural field model, composed of ON and OFF cells, with delayed all-to-all recurrent connections. As external spatially profiled inputs drive the network, ON cells receive inputs directly, while OFF cells receive an inverted image of the original signals. Via global and delayed inhibitory connections, these signals can cause the system to enter states of sustained oscillatory activity. We perform a bifurcation analysis of our model to elucidate how neural adaptation influences the ability of the network to exhibit oscillatory activity. We show that slow adaptation encourages input-induced rhythmic states by decreasing the Andronov-Hopf bifurcation threshold. We further determine how the feedback and adaptation together shape the resonant properties of the ON and OFF cell network and how this affects the response to time-periodic input. By introducing an additional frequency in the system, adaptation alters the resonance frequency by shifting the peaks where the response is maximal. We support these results with numerical experiments of the neural field model. Although developed in the context of the circuitry of the electric sense, these results are applicable to any network of spontaneously firing cells with global inhibitory feedback to themselves, in which a fraction of these cells receive external input directly, while the remaining ones receive an inverted version of this input via feedforward di-synaptic inhibition. Thus the results are relevant beyond the many sensory systems where ON and OFF cells are usually identified, and provide the backbone for understanding dynamical network effects of lateral connections and various forms of ON/OFF responses.

How noisy adaptation of neurons shapes interspike interval histograms and correlations

Channel noise is the dominant intrinsic noise source of neurons causing variability in the timing of action potentials and interspike intervals (ISI). Slow adaptation currents are observed in many cells and strongly shape response properties of neurons. These currents are mediated by finite populations of ionic channels and may thus carry a substantial noise component. Here we study the effect of such adaptation noise on the ISI statistics of an integrate-and-fire model neuron by means of analytical techniques and extensive numerical simulations. We contrast this stochastic adaptation with the commonly studied case of a fast fluctuating current noise and a deterministic adaptation current (corresponding to an infinite population of adaptation channels). We derive analytical approximations for the ISI density and ISI serial correlation coefficient for both cases. For fast fluctuations and deterministic adaptation, the ISI density is well approximated by an inverse Gaussian (IG) and the ISI correlations are negative. In marked contrast, for stochastic adaptation, the density is more peaked and has a heavier tail than an IG density and the serial correlations are positive. A numerical study of the mixed case where both fast fluctuations and adaptation channel noise are present reveals a smooth transition between the analytically tractable limiting cases. Our conclusions are furthermore supported by numerical simulations of a biophysically more realistic Hodgkin-Huxley type model. Our results could be used to infer the dominant source of noise in neurons from their ISI statistics.

Multimodal sensory responses of nucleus reticularis gigantocellularis and the responses' relation to cortical and motor activation

The connectivity of large neurons of the nucleus reticularis gigantocellularis (NRGc) in the medullary reticular formation potentially allows both for the integration of stimuli, in several modalities, that would demand immediate action, and for coordinated activation of cortical and motoric activity. We have simultaneously recorded cortical local field potentials, neck muscle electromyograph (EMG), and the neural activity of medullary NRGc neurons in unrestrained, unanesthetized rats to determine whether the activity of the NRGc is consistent with the modulation of general arousal. We observed excitatory responses of individual NRGc neurons to all modalities tested: tactile, visual, auditory, vestibular, and olfactory. Excitation was directly linked to increases in neck muscle EMG amplitude and corresponded with increases in the power of fast oscillations (30 to 80 Hz) of cortical activity and decreases in the power of slow oscillations (2 to 8 Hz). Because these reticular formation neurons can respond to broad ranges of stimuli with increased firing rates associated with the initiation of behavioral responses, we infer that they are part of an elementary "first responder" CNS arousal mechanism.

Spatiotemporal receptive field properties of a looming-sensitive neuron in solitarious and gregarious phases of the desert locust

Desert locusts (Schistocerca gregaria) can transform reversibly between the swarming gregarious phase and a solitarious phase, which avoids other locusts. This transformation entails dramatic changes in morphology, physiology, and behavior. We have used the lobula giant movement detector (LGMD) and its postsynaptic target, the descending contralateral movement detector (DCMD), which are visual interneurons that detect looming objects, to analyze how differences in the visual ecology of the two phases are served by altered neuronal function. Solitarious locusts had larger eyes and a greater degree of binocular overlap than those of gregarious locusts. The receptive field to looming stimuli had a large central region of nearly equal response spanning 120 degrees x 60 degrees in both phases. The DCMDs of gregarious locusts responded more strongly than solitarious locusts and had a small caudolateral focus of even further sensitivity. More peripherally, the response was reduced in both phases, particularly ventrally, with gregarious locusts showing greater proportional decrease. Gregarious locusts showed less habituation to repeated looming stimuli along the eye equator than did solitarious locusts. By contrast, in other parts of the receptive field the degree of habituation was similar in both phases. The receptive field organization to looming stimuli contrasts strongly with the receptive field organization of the same neurons to nonlooming local-motion stimuli, which show much more pronounced regional variation. The DCMDs of both gregarious and solitarious locusts are able to detect approaching objects from across a wide expanse of visual space, but phase-specific changes in the spatiotemporal receptive field are linked to lifestyle changes.

Precise subcellular input retinotopy and its computational consequences in an identified visual interneuron

The Lobula Giant Movement Detector (LGMD) is a higher-order visual interneuron of Orthopteran insects that responds preferentially to objects approaching on a collision course. It receives excitatory input from an entire visual hemifield that anatomical evidence suggests is retinotopic. We show that this excitatory projection activates calcium-permeable nicotinic acetylcholine receptors. In vivo calcium imaging reveals that the excitatory projection preserves retinotopy down to the level of a single ommatidium. Examining the impact of retinotopy on the LGMD's computational properties, we show that sublinear synaptic summation can explain orientation preference in this cell. Exploring retinotopy's impact on directional selectivity leads us to infer that the excitatory input to the LGMD is intrinsically directionally selective. Our results show that precise retinotopy has implications for the dendritic integration of visual information in a single neuron.

Role of spike-frequency adaptation in shaping neuronal response to dynamic stimuli

Spike-frequency adaptation is the reduction of a neuron's firing rate to a stimulus of constant intensity. In the locust, the Lobula Giant Movement Detector (LGMD) is a visual interneuron that exhibits rapid adaptation to both current injection and visual stimuli. Here, a reduced compartmental model of the LGMD is employed to explore adaptation's role in selectivity for stimuli whose intensity changes with time. We show that supralinearly increasing current injection stimuli are best at driving a high spike count in the response, while linearly increasing current injection stimuli (i.e., ramps) are best at attaining large firing rate changes in an adapting neuron. This result is extended with in vivo experiments showing that the LGMD's response to translating stimuli having a supralinear velocity profile is larger than the response to constant or linearly increasing velocity translation. Furthermore, we show that the LGMD's preference for approaching versus receding stimuli can partly be accounted for by adaptation. Finally, we show that the LGMD's adaptation mechanism appears well tuned to minimize sensitivity for the level of basal input.

The origin of adaptation in the auditory pathway of locusts is specific to cell type and function

We investigated the origin of spike frequency adaptation within a layered sensory network: the auditory pathway of locusts. Spike frequency adaptation as observed in an individual neuron may arise because of intrinsic or presynaptic adaptation mechanisms. To separate the contribution of different mechanisms, we recorded from the same cell during acoustic and intracellular current stimulation. We studied three identified neuron types that are representative for each network layer and participate in processing auditory patterns and localizing sound sources. By comparing current and acoustic stimulation, three distinct patterns of the distribution of adaptation mechanisms within the sensory network emerged: (1) balanced influence of both intrinsic and presynaptic adaptation mechanisms in an interneuron that summates over several receptor afferents (TN1), (2) predominantly inhibiting input as the source for spike frequency adaptation in a cell that transmits both pattern representation and directional information (BSN1), (3) primarily intrinsic, spike-triggered adaptation currents within an interneuron coding exclusively for direction (AN2). The time courses of spike frequency adaptation differed significantly between the cells types. Using the adaptation time constants, we were able to predict signal transmission properties for the different cells. We conclude that the adaptation mechanisms differ greatly among interneurons within this sensory pathway and are a function of their role in information processing.

Negative autoregulation linearizes the dose-response and suppresses the heterogeneity of gene expression

Although several recent studies have focused on gene autoregulation, the effects of negative feedback (NF) on gene expression are not fully understood. Our purpose here was to determine how the strength of NF regulation affects the characteristics of gene expression in yeast cells harboring chromosomally integrated transcriptional cascades that consist of the yEGFP reporter controlled by (i) the constitutively expressed tetracycline repressor TetR or (ii) TetR repressing its own expression. Reporter gene expression in the cascade without feedback showed a steep (sigmoidal) dose-response and a wide, nearly bimodal yEGFP distribution, giving rise to a noise peak at intermediate levels of induction. We developed computational models that reproduced the steep dose-response and the noise peak and predicted that negative autoregulation changes reporter expression from bimodal to unimodal and transforms the dose-response from sigmoidal to linear. Prompted by these predictions, we constructed a "linearizer" circuit by adding TetR autoregulation to our original cascade and observed a massive (7-fold) reduction of noise at intermediate induction and linearization of dose-response before saturation. A simple mathematical argument explained these findings and indicated that linearization is highly robust to parameter variations. These findings have important implications for gene expression control in eukaryotic cells, including the design of synthetic expression systems.

Spike frequency adaptation mediates looming stimulus selectivity in a collision-detecting neuron

How active membrane conductance dynamics tunes neurons for specific time-varying stimuli remains poorly understood. We studied the biophysical mechanisms by which spike frequency adaptation shapes visual stimulus selectivity in an identified visual interneuron of the locust. The lobula giant movement detector (LGMD) responds preferentially to objects approaching on a collision course with the locust. Using calcium imaging, pharmacology and modeling, we show that spike frequency adaptation in the LGMD is mediated by a Ca(2+)-dependent potassium conductance closely resembling those associated with 'small-conductance' (SK) channels. Intracellular block of this conductance minimally affected the LGMD's response to approaching stimuli, but substantially increased its response to translating ones. Thus, spike frequency adaptation contributes to the neuron's tuning by selectively decreasing its responses to nonpreferred stimuli. Our results identify a new mechanism by which spike frequency adaptation may tune visual neurons to behaviorally relevant stimuli.

Spike frequency adaptation mediates looming stimulus selectivity in a collision-detecting neuron

How active membrane conductance dynamics tunes neurons for specific time-varying stimuli remains poorly understood. We studied the biophysical mechanisms by which spike frequency adaptation shapes visual stimulus selectivity in an identified visual interneuron of the locust. The lobula giant movement detector (LGMD) responds preferentially to objects approaching on a collision course with the locust. Using calcium imaging, pharmacology and modeling, we show that spike frequency adaptation in the LGMD is mediated by a Ca(2+)-dependent potassium conductance closely resembling those associated with 'small-conductance' (SK) channels. Intracellular block of this conductance minimally affected the LGMD's response to approaching stimuli, but substantially increased its response to translating ones. Thus, spike frequency adaptation contributes to the neuron's tuning by selectively decreasing its responses to nonpreferred stimuli. Our results identify a new mechanism by which spike frequency adaptation may tune visual neurons to behaviorally relevant stimuli.

Spike-frequency adaptation generates intensity invariance in a primary auditory interneuron

Adaptation of the spike-frequency response to constant stimulation, as observed on various timescales in many neurons, reflects high-pass filter properties of a neuron's transfer function. Adaptation in general, however, is not sufficient to make a neuron's response independent of the mean intensity of a sensory stimulus, since low frequency components of the stimulus are still transmitted, although with reduced gain. We here show, based on an analytically tractable model, that the response of a neuron is intensity invariant, if the fully adapted steady-state spike-frequency response to constant stimuli is independent of stimulus intensity. Electrophysiological recordings from the AN1, a primary auditory interneuron of crickets, show that for intensities above 60 dB SPL (sound pressure level) the AN1 adapted with a time-constant of approximately 40 ms to a steady-state firing rate of approximately 100 Hz. Using identical random amplitude-modulation stimuli we verified that the AN1's spike-frequency response is indeed invariant to the stimulus' mean intensity above 60 dB SPL. The transfer function of the AN1 is a band pass, resulting from a high-pass filter (cutoff frequency at 4 Hz) due to adaptation and a low-pass filter (100 Hz) determined by the steady-state spike frequency. Thus, fast spike-frequency adaptation can generate intensity invariance already at the first level of neural processing.

Resonance or Integration? Self-Sustained Dynamics and Excitability of Neural Microcircuits

We investigated spontaneous activity and excitability in large networks of artificial spiking neurons. We compared three different spiking neuron models: integrate-and-fire (IF), regular-spiking (RS), and resonator (RES). First, we show that different models have different frequency-dependent response properties, yielding large differences in excitability. Then, we investigate the responsiveness of these models to a single afferent inhibitory/excitatory spike and calibrate the total synaptic drive such that they would exhibit similar peaks of the postsynaptic potentials (PSP). Based on the synaptic calibration, we build large microcircuits of IF, RS, and RES neurons and show that the resonance property favors homeostasis and self-sustainability of the network activity. On the other hand, integration produces instability while it endows the network with other useful properties, such as responsiveness to external inputs. We also investigate other potential sources of stable self-sustained activity and their relation to the membrane properties of neurons. We conclude that resonance and integration at the neuron level might interact in the brain to promote stability as well as flexibility and responsiveness to external input and that membrane properties, in general, are essential for determining the behavior of large networks of neurons.

Influence of electrotonic structure and synaptic mapping on the receptive field properties of a collision-detecting neuron

The lobula giant movement detector (LGMD) is a visual interneuron of Orthopteran insects involved in collision avoidance and escape behavior. The LGMD possesses a large dendritic field thought to receive excitatory, retinotopic projections from the entire compound eye. We investigated whether the LGMD's receptive field for local motion stimuli can be explained by its electrotonic structure and the eye's anisotropic sampling of visual space. Five locust (Schistocerca americana) LGMD neurons were stained and reconstructed. We show that the excitatory dendritic field and eye can be fitted by ellipsoids having similar geometries. A passive compartmental model fit to electrophysiological data was used to demonstrate that the LGMD is not electrotonically compact. We derived a spike rate to membrane potential transform using intracellular recordings under visual stimulation, allowing direct comparison between experimental and simulated receptive field properties. By assuming a retinotopic mapping giving equal weight to each ommatidium and equally spaced synapses, the model reproduced the experimental data along the eye equator, though it failed to reproduce the receptive field along the ventral-dorsal axis. Our results illustrate how interactions between the distribution of synaptic inputs and the electrotonic properties of neurons contribute to shaping their receptive fields.