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Notch signaling plays various roles in cell-fate specification through direct cell–cell interactions. Notch receptors are evolutionarily conserved transmembrane proteins with multiple epidermal growth factor (EGF)-like repeats. Drosophila Notch has 36 EGF-like repeats, and while some play a role in Notch signaling, the specific functions of most remain unclear. To investigate the role of each EGF-like repeat, we used 19 previously identified missense mutations of Notch with unique amino acid substitutions in various EGF-like repeats and a transmembrane domain; 17 of these were identified through a single genetic screen. We assessed these mutants’ phenotypes in the nervous system and hindgut during embryogenesis, and found that 10 of the 19 Notch mutants had defects in both lateral inhibition and inductive Notch signaling, showing context dependency. Of these 10 mutants, six accumulated Notch in the endoplasmic reticulum (ER), and these six were located in EGF-like repeats 8–10 or 25. Mutations with cysteine substitutions were not always coupled with ER accumulation. This suggests that certain EGF-like repeats may be particularly susceptible to structural perturbation, resulting in a misfolded and inactive Notch product that accumulates in the ER. Thus, we propose that these EGF-like repeats may be integral to Notch folding.

Inherent in visual scene analysis is a bottleneck associated with the need to sequentially sample locations with foveating eye movements. The concept of a ‘saliency map’ topographically encoding stimulus conspicuity over the visual scene has proven to be an efficient predictor of eye movements. Our work reviews insights into the neurobiological implementation of visual salience computation. We start by summarizing the role that different visual brain areas play in salience computation, whether at the level of feature analysis for bottom-up salience or at the level of goal-directed priority maps for output behaviour. We then delve into how a subcortical structure, the superior colliculus (SC), participates in salience computation. The SC represents a visual saliency map via a centre-surround inhibition mechanism in the superficial layers, which feeds into priority selection mechanisms in the deeper layers, thereby affecting saccadic and microsaccadic eye movements. Lateral interactions in the local SC circuit are particularly important for controlling active populations of neurons. This, in turn, might help explain long-range effects, such as those of peripheral cues on tiny microsaccades. Finally, we show how a combination of in vitro neurophysiology and large-scale computational modelling is able to clarify how salience computation is implemented in the local circuit of the SC. This article is part of the themed issue ‘Auditory and visual scene analysis’.

How do cells make efficient collective decisions during tissue morphogenesis? Humans and other organisms use feedback between movement and sensing known as ‘sensorimotor coordination’ or ‘active perception’ to inform behaviour, but active perception has not before been investigated at a cellular level within organs. Here we provide the first proof of concept in silico/in vivo study demonstrating that filopodia (actin-rich, dynamic, finger-like cell membrane protrusions) play an unexpected role in speeding up collective endothelial decisions during the time-constrained process of ‘tip cell’ selection during blood vessel formation (angiogenesis). We first validate simulation predictions in vivo with live imaging of zebrafish intersegmental vessel growth. Further simulation studies then indicate the effect is due to the coupled positive feedback between movement and sensing on filopodia conferring a bistable switch-like property to Notch lateral inhibition, ensuring tip selection is a rapid and robust process. We then employ measures from computational neuroscience to assess whether filopodia function as a primitive (basal) form of active perception and find evidence in support. By viewing cell behaviour through the ‘basal cognitive lens' we acquire a fresh perspective on the tip cell selection process, revealing a hidden, yet vital time-keeping role for filopodia. Finally, we discuss a myriad of new and exciting research directions stemming from our conceptual approach to interpreting cell behaviour. This article is part of the theme issue ‘Basal cognition: multicellularity, neurons and the cognitive lens’.

Changes in arousal influence cortical sensory representations, but the synaptic mechanisms underlying arousal-dependent modulation of cortical processing are unclear. Here, we use 2-photon Ca2+ imaging in the auditory cortex of awake mice to show that heightened arousal, as indexed by pupil diameter, broadens frequency-tuned activity of layer 2/3 (L2/3) pyramidal cells. Sensory representations are less sparse, and the tuning of nearby cells more similar when arousal increases. Despite the reduction in selectivity, frequency discrimination by cell ensembles improves due to a decrease in shared trial-to-trial variability. In vivo whole-cell recordings reveal that mechanisms contributing to the effects of arousal on sensory representations include state-dependent modulation of membrane potential dynamics, spontaneous firing, and tone-evoked synaptic potentials. Surprisingly, changes in short-latency tone-evoked excitatory input cannot explain the effects of arousal on the broadness of frequency-tuned output. However, we show that arousal strongly modulates a slow tone-evoked suppression of recurrent excitation underlying lateral inhibition [H. K. Kato, S. K. Asinof, J. S. Isaacson, Neuron, 95, 412–423, (2017)]. This arousal-dependent “network suppression” gates the duration of tone-evoked responses and regulates the broadness of frequency tuning. Thus, arousal can shape tuning via modulation of indirect changes in recurrent network activity.

Deep learning models are vulnerable to adversarial examples. As one of the most threatening types for practical deep learning systems, physical adversarial examples have received extensive attention in recent years. However, due to the insufficient focus on intrinsic characteristics such as model-agnostic features, existing studies generate adversarial perturbations with unsatisfactory transferability on attacking different models. Motivated by the viewpoint that attention reflects the intrinsic characteristics of the recognition process, we propose the Transferable Attention Attack (TA2) method to generate adversarial camouflages with strong transferable attacking ability by taking advantage of visual attention mechanism, i.e., triplet attention suppression. As for attacking, we generate transferable adversarial camouflages by distracting the model-shared similar attention patterns from the target to non-target regions, therefore promoting the transferable attacking ability. Furthermore, we enhance the attacking ability by converging the model attention of the non-ground-truth class, which exploits the lateral inhibition of visual models and activates the model perception for wrong classes. Besides, considering the visually suspicious appearance, we also introduce human attention to help improve their visual naturalness. We conduct extensive experiments in both the digital and physical worlds for classification tasks and comprehensively investigate the effectiveness of the discovered model attention mechanism, demonstrating that our method outperforms state-of-the-art methods.

Spiking Neural Networks (SNNs), as one of the algorithmic models in neuromorphic computing, have gained a great deal of research attention owing to temporal information processing capability, low power consumption, and high biological plausibility. The potential to efficiently extract spatio-temporal features makes it suitable for processing event streams. However, existing synaptic structures in SNNs are almost full-connections or spatial 2D convolution, neither of which can extract temporal dependencies adequately. In this work, we take inspiration from biological synapses and propose a spatio-temporal synaptic connection SNN (STSC-SNN) model to enhance the spatio-temporal receptive fields of synaptic connections, thereby establishing temporal dependencies across layers. Specifically, we incorporate temporal convolution and attention mechanisms to implement synaptic filtering and gating functions. We show that endowing synaptic models with temporal dependencies can improve the performance of SNNs on classification tasks. In addition, we investigate the impact of performance via varied spatial-temporal receptive fields and reevaluate the temporal modules in SNNs. Our approach is tested on neuromorphic datasets, including DVS128 Gesture (gesture recognition), N-MNIST, CIFAR10-DVS (image classification), and SHD (speech digit recognition). The results show that the proposed model outperforms the state-of-the-art accuracy on nearly all datasets.

One of the modern trends in the design of human–machine interfaces (HMI) is to involve the so called spiking neuron networks (SNNs) in signal processing. The SNNs can be trained by simple and efficient biologically inspired algorithms. In particular, we have shown that sensory neurons in the input layer of SNNs can simultaneously encode the input signal based both on the spiking frequency rate and on varying the latency in generating spikes. In the case of such mixed temporal-rate coding, the SNN should implement learning working properly for both types of coding. Based on this, we investigate how a single neuron can be trained with pure rate and temporal patterns, and then build a universal SNN that is trained using mixed coding. In particular, we study Hebbian and competitive learning in SNN in the context of temporal and rate coding problems. We show that the use of Hebbian learning through pair-based and triplet-based spike timing-dependent plasticity (STDP) rule is accomplishable for temporal coding, but not for rate coding. Synaptic competition inducing depression of poorly used synapses is required to ensure a neural selectivity in the rate coding. This kind of competition can be implemented by the so-called forgetting function that is dependent on neuron activity. We show that coherent use of the triplet-based STDP and synaptic competition with the forgetting function is sufficient for the rate coding. Next, we propose a SNN capable of classifying electromyographical (EMG) patterns using an unsupervised learning procedure. The neuron competition achieved via lateral inhibition ensures the “winner takes all” principle among classifier neurons. The SNN also provides gradual output response dependent on muscular contraction strength. Furthermore, we modify the SNN to implement a supervised learning method based on stimulation of the target classifier neuron synchronously with the network input. In a problem of discrimination of three EMG patterns, the SNN with supervised learning shows median accuracy 99.5% that is close to the result demonstrated by multi-layer perceptron learned by back propagation of an error algorithm.

•A biologically realistic rate-based model explains LFPs recorded via multi-contact electrodes across cortical laminae during exposure to best-frequency and non-best-frequency auditory stimuli.•The model fits were consistent between animals and recording sites.•The model exhibits plausible cellular dynamics, in particular parvalbumin-expressing interneurons being active in best-frequency conditions and somatostatin-expressing ones dominating in non-best-frequency conditions.•Results demonstrate the feasibility of the model-fitting approach in decomposing cell-type specific population activity, providing a foundation for further investigations into the dynamics of neural circuits underlying cortical sensory processing. Evoked neural responses to sensory stimuli have been extensively investigated in humans and animal models both to enhance our understanding of brain function and to aid in clinical diagnosis of neurological and neuropsychiatric conditions. Recording and imaging techniques such as electroencephalography (EEG), magnetoencephalography (MEG), local field potentials (LFPs), and calcium imaging provide complementary information about different aspects of brain activity at different spatial and temporal scales. Modeling and simulations provide a way to integrate these different types of information to clarify underlying neural mechanisms. In this study, we aimed to shed light on the neural dynamics underlying auditory evoked responses by fitting a rate-based model to LFPs recorded via multi-contact electrodes which simultaneously sampled neural activity across cortical laminae. Recordings included neural population responses to best-frequency (BF) and non-BF tones at four representative sites in primary auditory cortex (A1) of awake monkeys. The model considered major neural populations of excitatory, parvalbumin-expressing (PV), and somatostatin-expressing (SOM) neurons across layers 2/3, 4, and 5/6. Unknown parameters, including the connection strength between the populations, were fitted to the data. Our results revealed similar population dynamics, fitted model parameters, predicted equivalent current dipoles (ECD), tuning curves, and lateral inhibition profiles across recording sites and animals, in spite of quite different extracellular current distributions. We found that PV firing rates were higher in BF than in non-BF responses, mainly due to different strengths of tonotopic thalamic input, whereas SOM firing rates were higher in non-BF than in BF responses due to lateral inhibition. In conclusion, we demonstrate the feasibility of the model-fitting approach in identifying the contributions of cell-type specific population activity to stimulus-evoked LFPs across cortical laminae, providing a foundation for further investigations into the dynamics of neural circuits underlying cortical sensory processing.

This paper proposes an underwater image enhancement technology based on an artificial lateral inhibition network (ALIN) generated in the compound eye of a bionic horseshoe crab robot (BHCR). The concept of a horizontal suppression network is applied to underwater image processing with the aim of achieving low energy consumption, high efficiency processing, and adaptability to limited computing resources. The lateral inhibition network has the effect of “enhancing the center and suppressing the surroundings”. In this paper, a pattern recognition algorithm is used to compare and analyze the images obtained by an artificial lateral inhibition network and eight main underwater enhancement algorithms (white balance, histogram equalization, multi-scale Retinex, and dark channel). Therefore, we can evaluate the application of the artificial lateral inhibition network in underwater image enhancement and the deficiency of the algorithm. The experimental results show that the ALIN plays an obvious role in enhancing the important information in underwater image processing technology. Compared with other algorithms, this algorithm can effectively improve the contrast between the highlight area and the shadow area in underwater image processing, solve the problem that the information of the characteristic points of the collected image is not prominent, and achieve the unique effect of suppressing the intensity of other pixel points without information. Finally, we conduct target recognition verification experiments to assess the ALIN’s performance in identifying targets underwater with the BHCR in static water environments. The experiments confirm that the BHCR can maneuver underwater using multiple degrees of freedom (MDOF) and successfully acquire underwater targets.

In this study, we present a mixed-signal framework that utilizes MRAM (Magneto-resistive Random Access Memory) technology to emulate behaviors observed in biological neural networks on silicon substrates. While modern technology increasingly draws inspiration from biological neural networks, fully understanding these complex systems remains a significant challenge. Our framework integrates multi-bit MRAM synapse arrays and analog circuits to replicate essential neural functions, including Leaky Integrate and Fire (LIF) dynamics, Excitatory and Inhibitory Postsynaptic Potentials (EPSP and IPSP), the refractory period, and the lateral inhibition. A key challenge in using MRAM for neuromorphic systems is its low on/off resistance ratio, which limits the accuracy of current-mode analog computation. To overcome this, we introduce a current subtraction architecture that reliably generates multi-level synaptic currents based on MRAM states. This enables robust analog neural processing while preserving MRAM’s advantages, such as non-volatility and CMOS compatibility. The chip’s adjustable operating frequency allows it to replicate biologically realistic time scales as well as accelerate experimental processes. Experimental results from fabricated chips confirm the successful emulation of biologically inspired neural dynamics, demonstrating the feasibility of MRAM-based analog neuromorphic computation for real-time and scalable neural emulation.