Lateral Inhibition Network Research Articles

Research on Lateral Inhibition Network Based on Cell Membrane Electrical Model

Research on Lateral Inhibition Network Based on Cell Membrane Electrical Model

The Effect of Serotonin Receptor 5-HT1B on Lateral Inhibition between Spiny Projection Neurons in the Mouse Striatum.

The principal neurons of the striatum, the spiny projection neurons (SPNs), make inhibitory synaptic connections with each other via collaterals of their main axon, forming a local lateral inhibition network. Serotonin, acting via the 5-HT1B receptor, modulates neurotransmitter release from SPN terminals in striatal output nuclei, but the role of 5-HT1B receptors in lateral inhibition among SPNs in the striatum is unknown. Here, we report the effects of 5-HT1B receptor activation on lateral inhibition in the mouse striatum. Whole-cell recordings were made from SPNs in acute brain slices of either sex, while optogenetically activating presynaptic SPNs or fast-spiking interneurons (FSIs). Activation of 5-HT1B receptors significantly reduced the amplitude of IPSCs evoked by optical stimulation of both direct and indirect pathway SPNs. This reduction was blocked by application of a 5-HT1B receptor antagonist. Activation of 5-HT1B receptors did not reduce the amplitude of IPSCs evoked from FSIs. These results suggest a new role for serotonin as a modulator of lateral inhibition among striatal SPNs. The 5-HT1B receptor may, therefore, be a suitable target for future behavioral experiments investigating the currently unknown role of lateral inhibition in the function of the striatum.SIGNIFICANCE STATEMENT We show that stimulation of serotonin receptors reduces the efficacy of lateral inhibition between spiny projection neurons (SPNs), one of the biggest GABAergic sources in the striatum, by activation of the serotonin 5-HT1B receptor. The striatum receives serotonergic input from the dorsal raphe nuclei and is important in behavioral brain functions like learning and action selection. Our findings suggest a new role for serotonin in modulating the dynamics of neural interactions in the striatum, which extends current knowledge of the mechanisms of the behavioral effects of serotonin.

Accurate MR image super-resolution via lightweight lateral inhibition network

In recent years, convolutional neural networks (CNNs) have shown their advantages on MR image super-resolution (SR) tasks. Many current SR models, however, have heavy demands on computation and memory, which are not friendly to magnetic resonance imaging (MRI) where computing resource is usually constrained. On the other hand, a basic consideration in most MRI experiments is how to reduce scanning time to improve patient comfort and reduce motion artifacts. In this work, we ease the problem by presenting an effective and lightweight model that supports fast training and accurate SR inference. The proposed network is inspired by the lateral inhibition mechanism, which assumes that there exist inhibitory effects between adjacent neurons. The backbone of our network consists of several lateral inhibition blocks, where the inhibitory effect is explicitly implemented by a battery of cascaded local inhibition units. When model scale is small, explicitly inhibiting feature activations is expected to further explore model representational capacity. For more effective feature extraction, several parallel dilated convolutions are also used to extract shallow features directly from the input image. Extensive experiments on typical MR images demonstrate that our lateral inhibition network (LIN) achieves better SR performance than other lightweight models with similar model scale.

Drosophila Neuroblast Selection Is Gated by Notch, Snail, SoxB, and EMT Gene Interplay

In the developing Drosophila central nervous system (CNS), neural progenitor (neuroblast [NB]) selection is gated by lateral inhibition, controlled by Notch signaling and proneural genes. However, proneural mutants still generate many NBs, indicating the existence of additional proneural genes. Moreover, recent studies reveal involvement of key epithelial-mesenchymal transition (EMT) genes in NB selection, but the regulatory interplay between Notch signaling and the EMT machinery is unclear. We find that SoxNeuro (SoxB family) and worniu (Snail family) are integrated with the Notch pathway, and constitute the missing proneural genes. Notch signaling, the proneural, SoxNeuro, and worniu genes regulate key EMT genes to orchestrate the NB selection process. Hence, we uncover an expanded lateral inhibition network for NB selection and demonstrate its link to key players in the EMT machinery. The evolutionary conservation of the genes involved suggests that the Notch-SoxB-Snail-EMT network may control neural progenitor selection in many other systems.

Infrared image enhancement model based on gravitational force and lateral inhibition networks

Problems such as low contrast, noise, and edge blur are often encountered in color infrared images produced by infrared cameras. To solve these problems, we propose a new image enhancement algorithm based on the gravitational force and lateral inhibition network. First, information on total gravitational force for each dimension of the color infrared image was obtained. These two-dimensional three gray level images obtained using three-dimensional color properties help to define noise, edge and region within each dimension. Secondly, these three gray level images were subjected to a dual threshold value. A mean filter was used to reduce noise, while the lateral inhibition network was used for resolution and edge detection, and the regional gravity factor was used for contrast control. Finally, each dimension was combined again and a color enhanced image was produced. This study sets out to develop a method of enhancement images for infrared image analysis in cooling systems. The images used in the study are made up of a compressor, a condenser, and an evaporator belonging to the cooling system. The implementation of our method is simple and easy to understand and yields more accurate results. The experimental results show that the proposed method can eliminate noise, blur, and low contrast, and can also improve the details of infrared images better than other methods.

ConversationPiece II: Displaced and Rehacked

Conversations are amazing! Although we usually find the experience enjoyable and even relaxing, when one considers the difficulties of simultaneously generating signals that convey an intended message while at the same time trying to understand the messages of another, then the pleasures of conversation may seem rather surprising. We manage to communicate with each other without knowing quite what will happen next. We quickly manufacture precisely timed sounds and gestures on the fly, which we exchange with each other without clashing—even managing to slip in some imitations as we go along! Yet usually meaning is all we really notice. In the ConversationPiece project, we aim to transform conversations into musical sounds using neuro-inspired technology to expose the amazing world of sounds people create when talking with others. Sounds from a microphone are separated into different frequency bands by a computer-simulated “ear” (more precisely “basilar membrane”) and analyzed for tone onsets using a lateral-inhibition network, similar to some cortical neural networks. The detected events are used to generate musical notes played on a synthesizer either instantaneously or delayed. The first option allows for exchanging timed sound events between two speakers with a speech-like structure, but without conveying (much) meaning. Delayed feedback further allows self-exploration of one’s own speech. We discuss the current setup (ConversationPiece version II), insights from first experiments, and options for future applications.

Predictions of Speech Chimaera Intelligibility Using Auditory Nerve Mean-Rate and Spike-Timing Neural Cues.

Perceptual studies of speech intelligibility have shown that slow variations of acoustic envelope (ENV) in a small set of frequency bands provides adequate information for good perceptual performance in quiet, whereas acoustic temporal fine-structure (TFS) cues play a supporting role in background noise. However, the implications for neural coding are prone to misinterpretation because the mean-rate neural representation can contain recovered ENV cues from cochlear filtering of TFS. We investigated ENV recovery and spike-time TFS coding using objective measures of simulated mean-rate and spike-timing neural representations of chimaeric speech, in which either the ENV or the TFS is replaced by another signal. We (a) evaluated the levels of mean-rate and spike-timing neural information for two categories of chimaeric speech, one retaining ENV cues and the other TFS; (b) examined the level of recovered ENV from cochlear filtering of TFS speech; (c) examined and quantified the contribution to recovered ENV from spike-timing cues using a lateral inhibition network (LIN); and (d) constructed linear regression models with objective measures of mean-rate and spike-timing neural cues and subjective phoneme perception scores from normal-hearing listeners. The mean-rate neural cues from the original ENV and recovered ENV partially accounted for perceptual score variability, with additional variability explained by the recovered ENV from the LIN-processed TFS speech. The best model predictions of chimaeric speech intelligibility were found when both the mean-rate and spike-timing neural cues were included, providing further evidence that spike-time coding of TFS cues is important for intelligibility when the speech envelope is degraded.

Suppressive regulation of lateral inhibition between medium spiny neurons via dopamine D1 receptors in the rat nucleus accumbens shell

The nucleus accumbens (NAc) shell is closely associated with reward, psychiatric disorders (depression or schizophrenia), and drug abuse. Dopamine, released from the ventral tegmental area, is involved in these physiological functions and pathophysiological changes of NAc shell. Medium spiny neurons (MSNs), which are only GABAergic projection neurons in NAc, also innervate adjacent MSNs, forming the lateral inhibition network. Previous studies demonstrate that dopamine suppresses the lateral inhibition via D2-like (D2 and D3) receptors. However, the regulation to MSN–MSN synaptic transmission via D1 receptors remained unclear. In present study, aiming to reveal this issue, we examined the effects of the potent dopamine D1 receptor selective agonist SKF82958 on unitary IPSCs (uIPSCs) between two MSNs. SKF82958 (10μM) decreased the amplitude of uIPSCs in about half of MSNs. The actions of SKF82958 was eliminated by pre-application of SCH23390 (1μM), a dopamine D1 receptor selective antagonist. These results suggest that lateral inhibition between MSNs was suppressed via the activation of D1 receptors. Taken our previous findings, dopamine exclusively abolish the lateral inhibition in a stepwise pattern: (1) at low concentration of dopamine, only D3 receptors take part in the regulation of MSN–MSN synaptic transmissions, (2) dopamine concentration becomes higher, D2 receptors become involved in the suppression of lateral inhibition, and (3) at the maximal activity of the mesolimbic dopaminergic pathway, all dopamine receptor subtypes (i.e., D1, D2, and D3) are recruited for disinhibition of MSN activities.

Image enhancement algorithm based on improved lateral inhibition network

There is often substantial noise and blurred details in the images captured by cameras. To solve this problem, we propose a novel image enhancement algorithm combined with an improved lateral inhibition network. Firstly, we built a mathematical model of a lateral inhibition network in conjunction with biological visual perception; this model helped to realize enhanced contrast and improved edge definition in images. Secondly, we proposed that the adaptive lateral inhibition coefficient adhere to an exponential distribution thus making the model more flexible and more universal. Finally, we added median filtering and a compensation measure factor to build the framework with high pass filtering functionality thus eliminating image noise and improving edge contrast, addressing problems with blurred image edges. Our experimental results show that our algorithm is able to eliminate noise and the blurring phenomena, and enhance the details of visible and infrared images.

Reconstruction algorithm of super-resolution infrared image based on human vision processing mechanism

Aiming at solving the problem of low resolution and visual blur in infrared imaging, a super-resolution infrared image reconstruction method using human vision processing mechanism (HVPM) was proposed. This method combined a mechanism of vision lateral inhibition with an algorithm projection onto convex sets (POCS) reconstruction, the improved vision lateral inhibition network was utilized to enhance the contrast between object and background of low-resolution image sequences, then POCS algorithm was adopted to reconstruct super-resolution image. Experimental results showed that the proposed method can significantly improve the visual effect of image, whose contrast and information entropy of reconstructed infrared images were improved by approximately 5 times and 1.6 times compared with traditional POCS reconstruction algorithm, respectively.

An Improved Two-Dimensional OTSU Segmentation Method for Nvshu Character Image

For the structural characteristics of Chinese NvShu character, this paper proposes an improved two-dimensional (2-D) OTSU segmentation method based on the lateral inhibition network for Nvshu character image. A 2-D histogram with the gray level and the lateral inhibition level of the image was established and the maximum between-cluster variance was chosen as the criterion to select the optimal threshold. Experimental results show that the proposed method not only successfully reduced the effect of background noise, but also improved the accuracy of the character image segmentation, especially for NvShu character images with low contrast, uneven gray level of character strokes and uneven background

A biologically-inspired embedded monitoring network system for moving target detection in panoramic view

An embedded monitoring network system is based on the visual principle of compound eye, which meets the acquirements in field angle, detecting efficiency, and structural complexity of panoramic monitoring network. Three fixed wide-angle cameras are adopted as sub-eyes, and a main camera is installed on a high-speed platform. The system ensures the continuity of tracking with high sensitivity and accuracy in a field of view (FOV) of 360 × 180°. In the non-overlapping FOV of the sub-eyes, we adopt Gaussian background difference model and morphological algorithm to detect moving targets. However, in the overlapping FOV, we use the strategy of lateral inhibition network which improves the continuity of detection and speed of response. The experimental results show that our system locates a target within 0.15 s after it starts moving in the non-overlapping field; when a target moves in the overlapping field, it takes 0.23 s to locate it. The system reduces the cost and complexity in traditional panoramic monitoring network and lessens the labor intensity in the field of monitoring.

The dynamic model of lateral inhibition network and it is application

Concentrate on the computational model of visual attention, in this paper, we present the electric circuit model of the human retina cells on the base of lateral inhibit network and then deduce the mathematic formulas to define the static and dynamic processes of the network. The learning algorithm is also discussed to get right model parameters. The model reveals the case of the human biological character on image and video processing. Specifically on video processing, the convolution integral function which shows the neural computing process of the human eyes is developed based on the dynamical equation and the circuit principle. Some examples are also introduced to demonstrate the neural computing process of the model on the static image and video processing.

No Need for a Cognitive Map: Decentralized Memory for Insect Navigation

In many animals the ability to navigate over long distances is an important prerequisite for foraging. For example, it is widely accepted that desert ants and honey bees, but also mammals, use path integration for finding the way back to their home site. It is however a matter of a long standing debate whether animals in addition are able to acquire and use so called cognitive maps. Such a ‘map’, a global spatial representation of the foraging area, is generally assumed to allow the animal to find shortcuts between two sites although the direct connection has never been travelled before. Using the artificial neural network approach, here we develop an artificial memory system which is based on path integration and various landmark guidance mechanisms (a bank of individual and independent landmark-defined memory elements). Activation of the individual memory elements depends on a separate motivation network and an, in part, asymmetrical lateral inhibition network. The information concerning the absolute position of the agent is present, but resides in a separate memory that can only be used by the path integration subsystem to control the behaviour, but cannot be used for computational purposes with other memory elements of the system. Thus, in this simulation there is no neural basis of a cognitive map. Nevertheless, an agent controlled by this network is able to accomplish various navigational tasks known from ants and bees and often discussed as being dependent on a cognitive map. For example, map-like behaviour as observed in honey bees arises as an emergent property from a decentralized system. This behaviour thus can be explained without referring to the assumption that a cognitive map, a coherent representation of foraging space, must exist. We hypothesize that the proposed network essentially resides in the mushroom bodies of the insect brain.

基于自适应侧抑制网络的红外弱小目标检测

基于自适应侧抑制网络的红外弱小目标检测

The Application of Lateral Inhibition Model in Image's Contour Enhancement and Design of Its Electro-Model

For overcoming the problem s such as distortion and shift of object ’ s edge, easily losing the object detail information of method s in image edge detection , and satisfying higher demand for object detection in the modern war, a new image edge detection method was designed. LOG edge detection as a typical image processing method was introduced and the disadvantage of this model was analyzed firstly. B ased on lateral inhibition theory, an acyclic lateral inhibition network model (ALINM) based on biology vision information processing mechanism was design ed. The feasibility of object detection by lateral inhibition model was analyzed, i n order to express the advantage such as rapid calculati on easily real time operation of ALINM , the calculation magnitude of circulation difference lateral inhibition model was analyzed. Besides the correctness of ALINM wa s confirmed with two input cells, its transfer function wa s deduced. An algorithm of image edge detection based on this model wa s established finally, lateral inhibition effect also was confirmed by one-dimension and two-dimension circuit model based ALINM . S imulative experiment with different parameters and physics experiment prove that acyclic lateral inhibition network model can be realized easily, it can preserve the farthest detail information of object and has faster calculation speed than LOG operator . ALINM and lateral inhibition theory provide a useful method based on biology vision for object detection under difficult imaging conditions.

An Elementary-Motion-Detector Based on Hardware Lateral Inhibition Network

A hybrid analogy-digital system which imitates the compound eyes of insect for velocity evaluation has been proposed. The system enlarged measurement range of velocity by using a resistance network together with a D-trigger array instead of the time delay channel used in conventional elementary motion detector. The influence of background illumination on velocity evaluation is reduced by using lateral inhibition network. Moreover, the lateral inhibition network works in full hardware mode rather than processing image by software, allowing us to evaluate the velocity of a fast moving object. The feasibility of the system has been confirmed by the experiments.

BCM network develops orientation selectivity and ocular dominance in natural scene environment.

A two-eye visual environment is used in training a network of BCM neurons. We study the effect of misalignment between the synaptic density functions from the two eyes, on the formation of orientation selectivity and ocular dominance in a lateral inhibition network. The visual environment we use is composed of natural images. We show that for the BCM rule a natural image environment with binocular cortical misalignment is sufficient for producing networks with orientation-selective cells and ocular dominance columns. This work is an extension of our previous single cell misalignment model Shouval et al., 1996.

Neural networks simulating the frequency discrimination of hearing for non-stationary short tone stimuli

This paper addresses the question of frequency discrimination of hearing for non-stationary (short) tone stimuli (duration < or = 125 ms). Shortening of the stimulus duration leads to widening of the frequency spectrum of the tone. It can be shown that for hearing no acoustical uncertainty relation holds and thus some nonlinear elements must be present in hearing physiology. We present neurophysiological and psychoacoustical findings supporting the hypothesis that frequency discrimination of non-stationary short tone stimuli is performed in neural networks of the auditory system. Neural network architectures that could process the temporal and place excitation patterns originating in the cochlea are suggested. We show how these networks (temporal coincidence network processing the temporal code and lateral inhibition network processing the place code) can be combined to show performance consistent with auditory physiology. They might explain the frequency discrimination of hearing for non-stationary short tone stimuli. We show the fitting of psychophysical relations based on these networks with the experimentally determined data.

Neural dynamics in cortex-striatum co-cultures—II. Spatiotemporal characteristics of neuronal activity

Neural dynamics in organotypic cortex-striatum co-cultures grown for three to six weeks under conditions of dopamine deficiency are described. Single neuron activities were recorded intra- and extracellularly, and spatiotemporal spreading of population activity was mapped using voltage-sensitive dyes. The temporal properties of spike firing were characterized by interspike interval histograms, autocorrelation and crosscorrelation. Cortical pyramidal neurons (n = 40) showed irregular firing with a weak tendency to burst or to oscillate. Crosscorrelations revealed strong near-coincident firing and synaptic interactions. Disinhibition was a notable feature in a strongly firing cortical interneuron. Cortical activity spread in the co-culture, thus inducing an overall, homogeneous depolarization in the striatal part. Striatal cells were divided into principal cells and type I and II secondary cells. Principal cells (n = 40) were similar to those reported previously in vivo. Spiking activity ranged from irregular spiking at very low rates to episodic bursting, with an average burst duration of 1 s. Interspike intervals were single-peaked. Intracellular recordings revealed characteristic, long-lasting subthreshold depolarizations ("enabled state") that were shortened by local muscarinic receptor blockade. During prolonged time periods in the "enabled state", locally applied bicuculline induced strong firing in most principal neurons. Striatal secondary type I neurons (n = 25) showed high spiking rates, single- and double-peaked interval histograms and low-threshold, short-lasting stereotyped bursting activity and occasional rhythmic bursting. The firing of these neurons was increased by bicuculline. Crosscorrelations showed synchronization of these cells with principal cell activity. Secondary type II neurons (n = 15) revealed tonic, irregular firing patterns similar to cortical neurons, except with occasional firing in doublet spikes. We conclude that under conditions of dopamine deficiency in corticostriatal co-cultures (i) the cortex induces the "enabled" state and typical bursting mode in striatal principal neurons; (ii) principal neurons are strongly inhibited during the "enabled" state; (iii) muscarinic activity, presumably from tonically active striatal cholinergic interneurons, stabilizes the "enabled" state; (iv) striatal GABAergic interneurons receives synaptic inhibition and take part in synchronized activity among striatal principal cells. Our results favor the view of the striatum as a lateral inhibition network.