Lateral Inhibition Research Articles

Mimicking the retinal neuron functions by a photoresponsive single transistor with a double gate

To realize a low-cost neuromorphic visual system, employing an artificial neuron capable of mimicking the retinal neuron functions is essential. A photoresponsive single transistor neuron composed of a vertical silicon nanowire is proposed. Similar to retinal neurons, various photoresponsive characteristics of the single transistor neuron can be modulated by light intensity as well as wavelength and have a high responsivity to green light like the human eye. The device is designed with a cylindrical surrounding double-gate structure, enclosed by an independently controlled outer gate and inner gate. The outer gate has the function of selectively inhibiting neuron activity, which can mimic lateral inhibition of amacrine cells to ganglion cells, and the inner gate can be utilized for the adjustment of the firing threshold voltage, which can be used to mimic the regulation of photoresponsivity by horizontal cells for adaptive visual perception. Furthermore, a myelination function that controls the speed of information transmission is obtained according to the inherent asymmetric source/drain structure of a vertical silicon nanowire. This work can enable photoresponsive neuronal function using only a single transistor, providing a promising hardware implementation for building miniaturized neuromorphic vision systems at low cost.

Self-organization of modular activity in immature cortical networks

During development, cortical activity is organized into distributed modular patterns that are a precursor of the mature columnar functional architecture. Theoretically, such structured neural activity can emerge dynamically from local synaptic interactions through a recurrent network with effective local excitation with lateral inhibition (LE/LI) connectivity. Utilizing simultaneous widefield calcium imaging and optogenetics in juvenile ferret cortex prior to eye opening, we directly test several critical predictions of an LE/LI mechanism. We show that cortical networks transform uniform stimulations into diverse modular patterns exhibiting a characteristic spatial wavelength. Moreover, patterned optogenetic stimulation matching this wavelength selectively biases evoked activity patterns, while stimulation with varying wavelengths transforms activity towards this characteristic wavelength, revealing a dynamic compromise between input drive and the network’s intrinsic tendency to organize activity. Furthermore, the structure of early spontaneous cortical activity – which is reflected in the developing representations of visual orientation – strongly overlaps that of uniform opto-evoked activity, suggesting a common underlying mechanism as a basis for the formation of orderly columnar maps underlying sensory representations in the brain.

Novel Genome-Engineered H Alleles Differentially Affect Lateral Inhibition and Cell Dichotomy Processes during Bristle Organ Development.

Hairless (H) encodes the major antagonist in the Notch signaling pathway, which governs cellular differentiation of various tissues in Drosophila. By binding to the Notch signal transducer Suppressor of Hairless (Su(H)), H assembles repressor complexes onto Notch target genes. Using genome engineering, three new H alleles, HFA, HLLAA and HWA were generated and a phenotypic series was established by several parameters, reflecting the residual H-Su(H) binding capacity. Occasionally, homozygous HWA flies develop to adulthood. They were compared with the likewise semi-viable HNN allele affecting H-Su(H) nuclear entry. The H homozygotes were short-lived, sterile and flightless, yet showed largely normal expression of several mitochondrial genes. Typical for H mutants, both HWA and HNN homozygous alleles displayed strong defects in wing venation and mechano-sensory bristle development. Strikingly, however, HWA displayed only a loss of bristles, whereas bristle organs of HNN flies showed a complete shaft-to-socket transformation. Apparently, the impact of HWA is restricted to lateral inhibition, whereas that of HNN also affects the respective cell type specification. Notably, reduction in Su(H) gene dosage only suppressed the HNN bristle phenotype, but amplified that of HWA. We interpret these differences as to the role of H regarding Su(H) stability and availability.

Heterogeneous Forgetting Rates and Greedy Allocation in Slot-Based Memory Networks Promotes Signal Retention.

A key question in the neuroscience of memory encoding pertains to the mechanisms by which afferent stimuli are allocated within memory networks. This issue is especially pronounced in the domain of working memory, where capacity is finite. Presumably the brain must embed some "policy" by which to allocate these mnemonic resources in an online manner in order to maximally represent and store afferent information for as long as possible and without interference from subsequent stimuli. Here, we engage this question through a top-down theoretical modeling framework. We formally optimize a gating mechanism that projects afferent stimuli onto a finite number of memory slots within a recurrent network architecture. In the absence of external input, the activity in each slot attenuates over time (i.e., a process of gradual forgetting). It turns out that the optimal gating policy consists of a direct projection from sensory activity to memory slots, alongside an activity-dependent lateral inhibition. Interestingly, allocating resources myopically (greedily with respect to the current stimulus) leads to efficient utilization of slots over time. In other words, later-arriving stimuli are distributed across slots in such a way that the network state is minimally shifted and so prior signals are minimally "overwritten." Further, networks with heterogeneity in the timescales of their forgetting rates retain stimuli better than those that are more homogeneous. Our results suggest how online, recurrent networks working on temporally localized objectives without high-level supervision can nonetheless implement efficient allocation of memory resources over time.

Golden jackal optimization with lateral inhibition for image matching

Golden jackal optimization with lateral inhibition for image matching

A Role for Bottom-Up Alpha Oscillations in Temporal Integration.

Neural oscillations in the 8-12 Hz alpha band are thought to represent top-down inhibitory control and to influence temporal resolution: Individuals with faster peak frequencies segregate stimuli appearing closer in time. Recently, this theory has been challenged. Here, we investigate a special case in which alpha does not correlate with temporal resolution: when stimuli are presented amidst strong visual drive. Based on findings regarding alpha rhythmogenesis and wave spatial propagation, we suggest that stimulus-induced, bottom-up alpha oscillations play a role in temporal integration. We propose a theoretical model, informed by visual persistence, lateral inhibition, and network refractory periods, and simulate physiologically plausible scenarios of the interaction between bottom-up alpha and the temporal segregation. Our simulations reveal that different features of oscillations, including frequency, phase, and power, can influence temporal perception and provide a theoretically informed starting point for future empirical studies.

Processing difficulty while reading words with neighbors is not due to increased foveal load: Evidence from eye movements.

Words with high orthographic relatedness are termed "word neighbors" (angle/angel; birch/birth). Activation-based models of word recognition assume that lateral inhibition occurs between words and their activated neighbors. However, studies of eye movements during reading have not found inhibitory effects in early measures assumed to reflect lexical access (e.g., gaze duration). Instead, inhibition in eye-movement studies has been found in later measures of processing (e.g., total time, regressions in). We conducted an eye-movement boundary change study (Rayner, Cognitive Psychology, 7(1), 65-81, 1975) that manipulated the parafoveal preview of the word following the neighbor word (word N+1). In this way, we explored whether the late inhibitory effects seen with transposed letter words and words with higher-frequency neighbors result from reduced parafoveal preview due to increased foveal load and/or interference during late stages of lexical processing (the L2 stage within the E-Z Reader framework). For word N+1, while there were clear preview effects, there was not an effect of the neighborhood status of word N, nor a significant interaction. This suggests that the late inhibitory effects of earlier eye-movement studies are driven by misidentification of neighbor words rather than being due to increased foveal load.

Contribution of oxytocin and dopamine to the formation of neural clusters in the neocortex representing multimodal sensory stimuli

We have previously proposed a unified mechanism for the formation of contrasted representations of multimodal sensory stimuli in the activity of neocortical neurons. Contrasting is based on the opposite sign of modification of the efficacy of strong and weak excitatory inputs to the spiny cells of the striatum (the input structure of the basal ganglia) and the subsequent dopamine-dependent activity reorganizations in parallel cortico – basal ganglia – thalamocortical loops. Oxytocin and dopamine (through D1 receptors) can improve the contrast of these representations, contributing to the induction of LTP of the efficacy of excitation of cortical, thalamic, and hippocampal neurons innervating spiny cells. In addition, oxytocin and dopamine can improve contrasting enhancement by increasing the signal-to-noise ratio in the neocortex, hippocampus, and striatum. A proposed mechanism for increasing the signal-to-noise ratio is based on the opposite sign of a long-term modification of the efficacy of monosynaptic excitatory and disynaptic inhibitory inputs, simultaneously affecting the postsynaptic neuron. The proposed mechanisms may underlie the contribution of oxytocin and dopamine to improving the formation and long-term maintenance of activity in neuronal groups with similar receptive fields that form columns in the primary visual cortex, a tonotopic map in the primary auditory cortex, a somatotopic map in the sensorimotor cortex, and distributed clusters in the olfactory piriform cortex. These mechanisms differ from the commonly accepted mechanisms of the formation of neuronal clusters in the neocortex with similar RPs, that are based on afferent and lateral excitation and inhibition, which does not allow providing the specificity and duration of effects. Understanding the mechanisms of involvement of oxytocin and dopamine in the processing of multimodal sensory information may be useful for developing treatments for some disorders of social behavior.

Silencing CA1 pyramidal cells output reveals the role of feedback inhibition in hippocampal oscillations

The precise temporal coordination of neural activity is crucial for brain function. In the hippocampus, this precision is reflected in the oscillatory rhythms observed in CA1. While it is known that a balance between excitatory and inhibitory activity is necessary to generate and maintain these oscillations, the differential contribution of feedforward and feedback inhibition remains ambiguous. Here we use conditional genetics to chronically silence CA1 pyramidal cell transmission, ablating the ability of these neurons to recruit feedback inhibition in the local circuit, while recording physiological activity in mice. We find that this intervention leads to local pathophysiological events, with ripple amplitude and intrinsic frequency becoming significantly larger and spatially triggered local population spikes locked to the trough of the theta oscillation appearing during movement. These phenotypes demonstrate that feedback inhibition is crucial in maintaining local sparsity of activation and reveal the key role of lateral inhibition in CA1 in shaping circuit function.

Learning what matters: Synaptic plasticity with invariance to second-order input correlations.

Cortical populations of neurons develop sparse representations adapted to the statistics of the environment. To learn efficient population codes, synaptic plasticity mechanisms must differentiate relevant latent features from spurious input correlations, which are omnipresent in cortical networks. Here, we develop a theory for sparse coding and synaptic plasticity that is invariant to second-order correlations in the input. Going beyond classical Hebbian learning, our learning objective explains the functional form of observed excitatory plasticity mechanisms, showing how Hebbian long-term depression (LTD) cancels the sensitivity to second-order correlations so that receptive fields become aligned with features hidden in higher-order statistics. Invariance to second-order correlations enhances the versatility of biologically realistic learning models, supporting optimal decoding from noisy inputs and sparse population coding from spatially correlated stimuli. In a spiking model with triplet spike-timing-dependent plasticity (STDP), we show that individual neurons can learn localized oriented receptive fields, circumventing the need for input preprocessing, such as whitening, or population-level lateral inhibition. The theory advances our understanding of local unsupervised learning in cortical circuits, offers new interpretations of the Bienenstock-Cooper-Munro and triplet STDP models, and assigns a specific functional role to synaptic LTD mechanisms in pyramidal neurons.

Use-Dependent, Untapped Dual Kinase Signaling Localized in Brain Learning Circuitry.

Imaging brain learning and memory circuit kinase signaling is a monumental challenge. The separation of phases-based activity reporter of kinase (SPARK) biosensors allow circuit-localized studies of multiple interactive kinases in vivo, including protein kinase A (PKA) and extracellular signal-regulated kinase (ERK) signaling. In the precisely-mapped Drosophila brain learning/memory circuit, we find PKA and ERK signaling differentially enriched in distinct Kenyon cell connectivity nodes. We discover that potentiating normal circuit activity induces circuit-localized PKA and ERK signaling, expanding kinase function within new presynaptic and postsynaptic domains. Activity-induced PKA signaling shows extensive overlap with previously selective ERK signaling nodes, while activity-induced ERK signaling arises in new connectivity nodes. We find targeted synaptic transmission blockade in Kenyon cells elevates circuit-localized ERK induction in Kenyon cells with normally high baseline ERK signaling, suggesting lateral and feedback inhibition. We discover overexpression of the pathway-linking Meng-Po (human SBK1) serine/threonine kinase to improve learning acquisition and memory consolidation results in dramatically heightened PKA and ERK signaling in separable Kenyon cell circuit connectivity nodes, revealing both synchronized and untapped signaling potential. Finally, we find that a mechanically-induced epileptic seizure model (easily shocked "bang-sensitive" mutants) has strongly elevated, circuit-localized PKA and ERK signaling. Both sexes were used in all experiments, except for the hemizygous male-only seizure model. Hyperexcitable, learning-enhanced, and epileptic seizure models have comparably elevated interactive kinase signaling, suggesting a common basis of use-dependent induction. We conclude that PKA and ERK signaling modulation is locally coordinated in use-dependent spatial circuit dynamics underlying seizure susceptibility linked to learning/memory potential.

Cross-Modal Tinnitus Remediation: A Tentative Theoretical Framework.

Tinnitus is a prevalent hearing-loss deficit manifested as a phantom (internally generated by the brain) sound that is heard as a high-frequency tone in the majority of afflicted persons. Chronic tinnitus is debilitating, leading to distress, sleep deprivation, anxiety, and even suicidal thoughts. It has been theorized that, in the majority of afflicted persons, tinnitus can be attributed to the loss of high-frequency input from the cochlea to the auditory cortex, known as deafferentation. Deafferentation due to hearing loss develops with aging, which progressively causes tonotopic regions coding for the lost high-frequency coding to synchronize, leading to a phantom high-frequency sound sensation. Approaches to tinnitus remediation that demonstrated promise include inhibitory drugs, the use of tinnitus-specific frequency notching to increase lateral inhibition to the deafferented neurons, and multisensory approaches (auditory-motor and audiovisual) that work by coupling multisensory stimulation to the deafferented neural populations. The goal of this review is to put forward a theoretical framework of a multisensory approach to remedy tinnitus. Our theoretical framework posits that due to vision's modulatory (inhibitory, excitatory) influence on the auditory pathway, a prolonged engagement in audiovisual activity, especially during daily discourse, as opposed to auditory-only activity/discourse, can progressively reorganize deafferented neural populations, resulting in the reduced synchrony of the deafferented neurons and a reduction in tinnitus severity over time.

Attention‐based prohibited item detection in X‐ray images during security checking

AbstractThis paper focuses on the intelligent detection of prohibited items in X‐ray images during the security checking process. An intelligent semantic segmentation model of prohibited items in X‐ray images is proposed based on the attention‐based object localization method. Based on the pre‐trained CNN classification framework, the attention mechanism can map the high‐layer semantic information of objects into the input space, while generating energy saliency maps to locate the prohibited items. In order to make the obtained attention maps discriminative, the lateral and contrastive inhibition strategies are introduced and combined together which can highlight the responses of activated neurons. Under the guidance of attention responses, two traditional image segmentation algorithms are employed to achieve the semantic segmentation results for the prohibited items detection in X‐ray images. The proposed semantic segmentation model relies on weakly supervised learning mechanism, and only depends on the category labels of prohibited items, which greatly avoids the work cost of data semantic annotation. The experimental results based on the public SIXray baseline and the self‐built X‐ray image database demonstrate the proposed method can achieve about 65% IoU localization precise averagely. In addition, comparison experiments were carried out with the state‐of‐the‐arts and ablation experiments to verify the effectiveness of the proposed model.

SynapNet: A Complementary Learning System Inspired Algorithm With Real-Time Application in Multimodal Perception.

Catastrophic forgetting is a phenomenon in which a neural network, upon learning a new task, struggles to maintain its performance on previously learned tasks. It is a common challenge in the realm of continual learning (CL) through neural networks. The mammalian brain addresses catastrophic forgetting by consolidating memories in different parts of the brain, involving the hippocampus and the neocortex. Taking inspiration from this brain strategy, we present a CL framework that combines a plastic model simulating the fast learning capabilities of the hippocampus and a stable model representing the slow consolidation nature of the neocortex. To supplement this, we introduce a variational autoencoder (VAE)-based pseudo memory for rehearsal purposes. In addition by applying lateral inhibition masks on the gradients of the convolutional layer, we aim at damping the activity of adjacent neurons and introduce a sleep phase to reorganize the learned representations. Empirical evaluation demonstrates the positive impact of such additions on the performance of our proposed framework; we evaluate the proposed model on several class-incremental and domain-incremental datasets and compare it with the standard benchmark algorithms, showing significant improvements. With the aim to showcase practical applicability, we implement the algorithm in a physical environment for object classification using a soft pneumatic gripper. The algorithm learns new classes incrementally in real time and also exhibits significant backward knowledge transfer (KT).

Biomimetic Measurement Method for Surface Electric Potential Imaging Inspired by Visual Lateral Inhibition

Biomimetic Measurement Method for Surface Electric Potential Imaging Inspired by Visual Lateral Inhibition

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

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

Low dose ribosomal DNA P-loop mutation affects development and enforces autophagy in Arabidopsis

ABSTRACT Arabidopsis contains hundreds of ribosomal DNA copies organized within the nucleolar organizing regions (NORs) in chromosomes 2 and 4. There are four major types of variants of rDNA, VAR1–4, based on the polymorphisms of 3’ external transcribed sequences. The variants are known to be differentially expressed during plant development. We created a mutant by the CRISPR-Cas9-mediated excision of ~ 25 nt from predominantly NOR4 ribosomal DNA copies, obtaining mosaic mutational events on ~ 5% of all rDNA copies. The excised region consists of P-loop and Helix-82 segments of 25S rRNA. The mutation led to allelic, dosage-dependent defects marked by lateral root inhibition, reduced size, and pointy leaves, all previously observed for defective ribosomal function. The mutation in NOR4 led to dosage compensation from the NOR2 copies by elevated expression of VAR1 in mutants and further associated single-nucleotide variants, thus, resulting in altered rRNA sub-population. Furthermore, the mutants exhibited rRNA maturation defects specifically in the minor pathway typified by 32S pre-rRNA accumulation. Density-gradient fractionation and subsequent RT-PCR of rRNA analyses revealed that mutated copies were not incorporated into the translating ribosomes. The mutants in addition displayed an elevated autophagic flux as shown by the autophagic marker GFP-ATG8e, likely related to ribophagy.

Dynamic altruistic cooperation within breast tumors

BackgroundSocial behaviors such as altruism, where one self-sacrifices for collective benefits, critically influence an organism’s survival and responses to the environment. Such behaviors are widely exemplified in nature but have been underexplored in cancer cells which are conventionally seen as selfish competitive players. This multidisciplinary study explores altruism and its mechanism in breast cancer cells and its contribution to chemoresistance.MethodsMicroRNA profiling was performed on circulating tumor cells collected from the blood of treated breast cancer patients. Cancer cell lines ectopically expressing candidate miRNA were used in co-culture experiments and treated with docetaxel. Ecological parameters like relative survival and relative fitness were assessed using flow cytometry. Functional studies and characterization performed in vitro and in vivo include proliferation, iTRAQ-mass spectrometry, RNA sequencing, inhibition by small molecules and antibodies, siRNA knockdown, CRISPR/dCas9 inhibition and fluorescence imaging of promoter reporter-expressing cells. Mathematical modeling based on evolutionary game theory was performed to simulate spatial organization of cancer cells.ResultsOpposing cancer processes underlie altruism: an oncogenic process involving secretion of IGFBP2 and CCL28 by the altruists to induce survival benefits in neighboring cells under taxane exposure, and a self-sacrificial tumor suppressive process impeding proliferation of altruists via cell cycle arrest. Both processes are regulated concurrently in the altruists by miR-125b, via differential NF-κB signaling specifically through IKKβ. Altruistic cells persist in the tumor despite their self-sacrifice, as they can regenerate epigenetically from non-altruists via a KLF2/PCAF-mediated mechanism. The altruists maintain a sparse spatial organization by inhibiting surrounding cells from adopting the altruistic fate via a lateral inhibition mechanism involving a GAB1-PI3K-AKT-miR-125b signaling circuit.ConclusionsOur data reveal molecular mechanisms underlying manifestation, persistence and spatial spread of cancer cell altruism. A minor population behave altruistically at a cost to itself producing a collective benefit for the tumor, suggesting tumors to be dynamic social systems governed by the same rules of cooperation in social organisms. Understanding cancer cell altruism may lead to more holistic models of tumor evolution and drug response, as well as therapeutic paradigms that account for social interactions. Cancer cells constitute tractable experimental models for fields beyond oncology, like evolutionary ecology and game theory.

Synaptic plasticity at the dentate gyrus granule cell to somatostatin-expressing interneuron synapses supports object location memory.

Somatostatin-expressing interneurons (SOMIs) in the mouse dentate gyrus (DG) receive feedforward excitation from granule cell (GC) mossy fiber (MF) synapses and provide feedback lateral inhibition onto GC dendrites to support environment representation in the DG network. Although this microcircuitry has been implicated in memory formation, little is known about activity-dependent plastic changes at MF-SOMI synapses and their influence on behavior. Here, we report that the metabotropic glutamate receptor 1α (mGluR1α) is required for the induction of associative long-term potentiation (LTP) at MF-SOMI synapses. Pharmacological block of mGluR1α, but not mGluR5, prevented synaptic weight changes. LTP at MF-SOMI synapses was postsynaptically induced, required increased intracellular Ca2+, involved G-protein-mediated and Ca2+-dependent (extracellular signal-regulated kinase) ERK1/2 pathways, and the activation of NMDA receptors. Specific knockdown of mGluR1α in DG-SOMIs by small hairpin RNA expression prevented MF-SOMI LTP, reduced SOMI recruitment, and impaired object location memory. Thus, postsynaptic mGluR1α-mediated MF-plasticity at SOMI input synapses critically supports DG-dependent mnemonic functions.

Nonlinear control of neurodegenerative diseases. A case study on optical illusion networks disrupted by diabetic retinopathy

We present an efficient computational framework for the design of optimal drug delivery control strategies that can successfully treat a family of neurodegenerative diseases that originate from channelopathies and synaptopathies. To this end, we extend our previously introduced scalable and adaptable modelling framework that models heterogeneous Hodgkin–Huxley (HH) neuronal networks to account for the modular organisation of the neurons in the brain, e.g. interconnecting sub-networks of heterogeneous neurons. Based on this framework, we introduce a novel design of lateral inhibition networks to successfully reproduce 2D optical illusions that are known to occur in the human retina. We model the dynamic behaviour of Diabetic Retinopathy (DR), a neurodegenerative disease that progressively hinders the inherent ability of patients to distinguish optical illusions. We implement nonlinear control on accurate models of diabetic lateral inhibition neuronal networks to recover their functionality and investigate the effects of virtual drug administration. We utilise the healthy and diabetic optical illusions generated by these networks as a ‘computational’ phenotype to design therapies based on an adaptive terminal error iterative learning controller (TE-ILC). Therefore, we provide a comprehensive computational framework that is able to imitate the dynamics of healthy and diseased neuronal networks and we propose an adaptive nonlinear control strategy based on the error between output images that correspond to healthy and diseased conditions.