Sparse Activity Research Articles

Highly scalable In0.95Ga0.05As based controllable leaky-integrate-fire neuron for high performance spiking neural network and applications

Abstract In this work, we design and simulate a leaky integrate-and-fire (LIF) neuron based on a single Indium Gallium Arsenide (In0.95Ga0.05As) MOSFET. The proposed implementation results in significant improvements in energy, area, and cost reduction. The In0.95Ga0.05As -MOSFET LIF neuron imitates neuron behaviour accurately, with low breakdown voltage, high impact ionization coefficient, and sharp breakdown. Due to the storage of induced holes in the potential well of the In0.95Ga0.05As -MOSFET, impact ionisation is the main mechanism creating the spiking characteristic in this case. It requires only 7.11 pJ/spike of energy, compared to the silicon-based SOI MOSFET, which requires 45 pJ/spike. The LIF neuron exhibits a 0.5 MHz high spiking frequency, which is 5 times higher than real nerves in the human body. Compared to biology, MHz operation provides appealing hardware acceleration. In0.95Ga0.05As -MOSFET LIF neuron firing may be controlled through the application of gate voltage, which can increase the spiking neural networks (SNN) energy efficiency by causing sparse activity. The effects of gate metal work function, Gallium mole fraction, and temperature on spike current variations are also investigated. This neuron circuit has promising potential for large-scale integration in spiking neural network hardware. Reconfigurable threshold logic gates have been investigated for the proposed neuron in order to implement universal digital logic functions, such as NAND and NOR.

Heterogeneous provenance and intracontinental rift evidence in the early–middle Jurassic La Boca Formation in the Huizachal-Peregrina Anticlinorium, Mexico

ABSTRACT The Early–Middle Jurassic Nazas continental rift province in Mexico has been debated as either an extensional continental arc due to eastward subduction beneath North America or an intracontinental rift linked to Pangea break-up. New petrographic and U-Pb geochronological data from the La Boca Formation near Ciudad Victoria – a key locality within this province – reveal diverse clastic sources, forming three provenance groups. Group 1 (Juan Capitán Member) crops out in the Huizachal Valley and consists of volcaniclastic fluvial deposits derived from a local volcanic centre. Group 2 (Agua de Las Minas Member), also found in the Huizachal Valley, overlies Group 1 and has mixed provenance from local volcanic and surrounding basement uplift sources. Both groups transition laterally to deposits of Group 3 (San Pedro Member) to the north, which consists of metamorphiclastic fluvial deposits from the eastern basement uplift and is not influenced by the volcanic centre. The rapid dilution of volcanic detritus within ~35 km highlights the limited extent of the volcanic centre as a source of sediment. Our results suggest that the Nazas province was more likely formed in a continental rift basin with sparse volcanic activity linked to Pangea break-up rather than as an extensive volcanic arc.

Stability of cross-sensory input to primary somatosensory cortex across experience

Merging information across sensory modalities is key to forming robust percepts, yet how the brain achieves this feat remains unclear. Recent studies report cross-modal influences in the primary sensory cortex, suggesting possible multisensory integration in the early stages of cortical processing. We test several hypotheses about the function of auditory influences on mouse primary somatosensory cortex (S1) using invivo two-photon calcium imaging. We found sound-evoked spiking activity in an extremely small fraction of cells, and this sparse activity did not encode auditory stimulus identity. Moreover, S1 did not encode information about specific audio-tactile feature conjunctions. Auditory and audio-tactile stimulus encoding remained unchanged after both passive experience and reinforcement. These results suggest that while primary sensory cortex is plastic within its own modality, the influence of other modalities is remarkably stable and stimulus nonspecific.

Discovering plasticity rules that organize and maintain neural circuits.

Intrinsic dynamics within the brain can accelerate learning by providing a prior scaffolding for dynamics aligned with task objectives. Such intrinsic dynamics should self-organize and self-sustain in the face of fluctuating inputs and biological noise, including synaptic turnover and cell death. An example of such dynamics is the formation of sequences, a ubiquitous motif in neural activity. The sequence-generating circuit in zebra finch HVC provides a reliable timing scaffold for motor output in song and demonstrates a remarkable capacity for unsupervised recovery following perturbation. Inspired by HVC, we seek a local plasticity rule capable of organizing and maintaining sequence-generating dynamics despite continual network perturbations. We adopt a meta-learning approach introduced by Confavreux et al, which parameterizes a learning rule using basis functions constructed from pre- and postsynaptic activity and synapse size, with tunable time constants. Candidate rules are simulated within initially random networks, and their fitness is evaluated according to a loss function that measures the fidelity with which the resulting dynamics encode time. We use this approach to introduce biological noise, forcing meta-learning to find robust solutions. We first show that, in the absence of perturbation, meta-learning identifies a temporally asymmetric generalization of Oja's rule that reliably organizes sparse sequential activity. When synaptic turnover is introduced, the learned rule incorporates an additional form of homeostasis, better maintaining sequential dynamics relative to other previously proposed rules. Additionally, inspired by recent findings demonstrating plasticity in synapses from inhibitory interneurons in HVC, we explore the role of inhibitory plasticity in sequence-generating circuits. We find that learned plasticity adjusts both excitation and inhibition in response to manipulations, outperforming rules applied only to excitatory connections. We demonstrate how plasticity acting on both excitatory and inhibitory synapses can better shape excitatory cell dynamics to scaffold timing representations.

Cemeteries are active urban resonant places, or are they not?

A cemetery is usually an integral part of the urban environment, constituting a landmark, where a city buries its deceased, while providing places of remembrance and memory. Therefore, it establishes wide, usually car-free areas, where trees and small animals dominate the environmental space among the graves, within its urban plan. These seemingly empty areas, also host sparse human activity by mourners, visitors, death professionals or cleaners; it is their resonances, along with an ambient city soundscape from the background, that a listener experiences when visiting. Considering listening as multisensory experience, imaginary sounds often seem to integrate in this soundscape. What can sounds of the cemetery reveal? And which sound qualities define the relation between actual and imaginary sounds? By employing sound ethnography, including observation, recordings, listening tests and interviews, the article traces the qualities of Limassol's urban cemeteries through their everyday sound, from an experiential perspective.

Dense and persistent odor representations in the olfactory bulb of awake mice.

Recording and analysis of neural activity is often biased toward detecting sparse subsets of highly active neurons, masking important signals carried in low magnitude and variable responses. To investigate the contribution of seemingly noisy activity to odor encoding, we used mesoscale calcium imaging from mice of both sexes to record odor responses from the dorsal surface of bilateral olfactory bulbs (OBs). The outer layer of the mouse OB is comprised of dendrites organized into discrete "glomeruli", which are defined by odor receptor-specific sensory neuron input. We extracted activity from a large population of glomeruli and used logistic regression to classify odors from individual trials with high accuracy. We then used add-in and drop-out analyses to determine subsets of glomeruli necessary and sufficient for odor classification. Classifiers successfully predicted odor identity even after excluding sparse, highly active glomeruli, indicating that odor information is redundantly represented across a large population of glomeruli. Additionally, we found that Random Forest feature selection informed by Gini Inequality (RFGI) reliably ranked glomeruli by their contribution to overall odor classification. RFGI provided a measure of "feature importance" for each glomerulus that correlated with intuitive features like response magnitude. Finally, in agreement with previous work, we found that odor information persists in glomerular activity after odor offset. Together, our findings support a model of olfactory bulb odor coding where sparse activity is sufficient for odor identification, but information is widely, redundantly available across a large population of glomeruli, with each glomerulus representing information about more than one odor.Significance statement This study leverages meso-scale imaging and machine learning to investigate how odor information is first represented in the brain. Typically, recordings of neuronal activity focus on active individual cells, potentially overlooking broader variations in neuronal responses across populations. Our results demonstrate that a considerable amount of olfactory information is redundantly distributed across a large proportion of olfactory bulb glomeruli. Even after excluding a majority of glomeruli, odor identification remained possible. These findings indicate that, although a few glomeruli are sufficient for odor recognition, an abundance of additional information is represented across a broad population. Understanding how the brain manages redundant olfactory information will shed light on its adaptive mechanisms for navigating diverse real-world circumstances and responding to fluctuating internal states.

A comprehensive data-driven model of cat primary visual cortex.

Knowledge integration based on the relationship between structure and function of the neural substrate is one of the main targets of neuroinformatics and data-driven computational modeling. However, the multiplicity of data sources, the diversity of benchmarks, the mixing of observables of different natures, and the necessity of a long-term, systematic approach make such a task challenging. Here we present a first snapshot of a long-term integrative modeling program designed to address this issue in the domain of the visual system: a comprehensive spiking model of cat primary visual cortex. The presented model satisfies an extensive range of anatomical, statistical and functional constraints under a wide range of visual input statistics. In the presence of physiological levels of tonic stochastic bombardment by spontaneous thalamic activity, the modeled cortical reverberations self-generate a sparse asynchronous ongoing activity that quantitatively matches a range of experimentally measured statistics. When integrating feed-forward drive elicited by a high diversity of visual contexts, the simulated network produces a realistic, quantitatively accurate interplay between visually evoked excitatory and inhibitory conductances; contrast-invariant orientation-tuning width; center surround interactions; and stimulus-dependent changes in the precision of the neural code. This integrative model offers insights into how the studied properties interact, contributing to a better understanding of visual cortical dynamics. It provides a basis for future development towards a comprehensive model of low-level perception.

Stability of cross-sensory input to primary somatosensory cortex across experience.

Merging information from across sensory modalities is key to forming robust, disambiguated percepts of the world, yet how the brain achieves this feat remains unclear. Recent observations of cross-modal influences in primary sensory cortical areas have suggested that multisensory integration may occur in the earliest stages of cortical processing, but the role of these responses is still poorly understood. We address these questions by testing several hypotheses about the possible functions served by auditory influences on the barrel field of mouse primary somatosensory cortex (S1) using in vivo 2-photon calcium imaging. We observed sound-evoked spiking activity in a small fraction of cells overall, and moreover that this sparse activity was insufficient to encode auditory stimulus identity; few cells responded preferentially to one sound or another, and a linear classifier trained to decode auditory stimuli from population activity performed barely above chance. Moreover S1 did not encode information about specific audio-tactile feature conjunctions that we tested. Our ability to decode auditory audio-tactile stimuli from neural activity remained unchanged after both passive experience and reinforcement. Collectively, these results suggest that while a primary sensory cortex is highly plastic with regard to its own modality, the influence of other modalities are remarkably stable and play a largely stimulus-non-specific role.

Memory’s gatekeeper: The role of PFC in the encoding of congruent events

Theoretical models conventionally portray the consolidation of memories as a slow process that unfolds during sleep. According to the classical Complementary Learning Systems theory, the hippocampus (HPC) rapidly changes its connectivity during wakefulness to encode ongoing events and create memory ensembles that are later transferred to the prefrontal cortex (PFC) during sleep. However, recent experimental studies challenge this notion by showing that new information consistent with prior knowledge can be rapidly consolidated in PFC during wakefulness and that PFC lesions disrupt the encoding of congruent events in the HPC. The contributions of the PFC to memory encoding have therefore largely been overlooked. Moreover, most theoretical frameworks assume random and uncorrelated patterns representing memories, disregarding the correlations between our experiences. To address these shortcomings, we developed a HPC-PFC network model that simulates interactions between the HPC and PFC during the encoding of a memory (awake stage), and subsequent consolidation (sleeping stage) to examine the contributions of each region to the consolidation of novel and congruent memories. Our results show that the PFC network uses stored memory "schemas" consolidated during previous experiences to identify inputs that evoke congruent patterns of activity, quickly integrate it into its network, and gate which components are encoded in the HPC. More specifically, the PFC uses GABAergic long-range projections to inhibit HPC neurons representing input components correlated with a previously stored memory "schema," eliciting sparse hippocampal activity during exposure to congruent events, as it has been experimentally observed.

KinomeMETA: a web platform for kinome-wide polypharmacology profiling with meta-learning.

Kinase-targeted inhibitors hold promise for new therapeutic options, with multi-target inhibitors offering the potential for broader efficacy while minimizing polypharmacology risks. However, comprehensive experimental profiling of kinome-wide activity is expensive, and existing computational approaches often lack scalability or accuracy for understudied kinases. We introduce KinomeMETA, an artificial intelligence (AI)-powered web platform that significantly expands the predictive range with scalability for predicting the polypharmacological effects of small molecules across the kinome. By leveraging a novel meta-learning algorithm, KinomeMETA efficiently utilizes sparse activity data, enabling rapid generalization to new kinase tasks even with limited information. This significantly expands the repertoire of accurately predictable kinases to 661 wild-type and clinically-relevant mutant kinases, far exceeding existing methods. Additionally, KinomeMETA empowers users to customize models with their proprietary data for specific research needs. Case studies demonstrate its ability to discover new active compounds by quickly adapting to small dataset. Overall, KinomeMETA offers enhanced kinome virtual profiling capabilities and is positioned as a powerful tool for developing new kinase inhibitors and advancing kinase research. The KinomeMETA server is freely accessible without registration at https://kinomemeta.alphama.com.cn/.

Highly biomimetic spiking neuron using SiGe heterojunction bipolar transistors for energy-efficient neuromorphic systems

We demonstrate a highly biomimetic spiking neuron capable of fast and energy-efficient neuronal oscillation dynamics. Our simple neuron circuit is constructed using silicon–germanium heterojunction based bipolar transistors (HBTs) with nanowire structure. The HBT has a hysteresis window with steep switching characteristics and high current margin in the low voltage range, which enables a high spiking frequency (~ 245 kHz) with low energy consumption (≤ 1.37 pJ/spike). Also, gated structure achieves a stable balance in the activity of the neural system by incorporating both excitatory and inhibitory signal. Furthermore, inhibition of multiple strengths can be realized by adjusting the integration time according to the amplitude of the inhibitory signal. In addition, the spiking frequency can be tuned by mutually controlling the hysteresis window in the HBTs. These results ensure the sparse activity and homeostasis of neural networks.

BrainForest: Neuromorphic Multiplier-Less Bit-Serial Weight-Memory-Optimized 1024-Tree Brain-State Classification Processor.

Personalized brain implants have the potential to revolutionize the treatment of neurological disorders and augment cognition. Medical implants that deliver therapeutic stimulation in response to detected seizures have already been deployed for the treatment of epilepsy. These devices require low-power integrated circuits for life-long operation. This constraint impedes the integration of machine-learning driven classifiers that could improve treatment outcomes. This paper introduces BrainForest, a neuromorphic multiplier-less bit-serial weight-memory-optimized brain-state classification processor. The architecture achieves state-of-the-art energy efficiency using two layers of neuron models to implement the spectral and temporal functions needed for classification: 1) resonate-and-fire neurons are used to extract physiological signal band energy EEG biomarkers 2) leaky integrator neurons are used to build multi-timescale representations for classification. Sparse neural model firing activity is used to clock-gate device logic, thereby decreasing power consumption by 93%. An energy-optimized 1024-tree boosted decision forest performs the classification used to trigger stimulation in response to detected pathological brain states. The IC is implemented in 65nm CMOS with state-of-the-art power consumption (best case: 9.6μW, typical: 118μW), achieving a seizure sensitivity of 97.5% with a false detection rate of 2.08 per hour.

Hippocampal formation-inspired global self-localization: quick recovery from the kidnapped robot problem from an egocentric perspective.

It remains difficult for mobile robots to continue accurate self-localization when they are suddenly teleported to a location that is different from their beliefs during navigation. Incorporating insights from neuroscience into developing a spatial cognition model for mobile robots may make it possible to acquire the ability to respond appropriately to changing situations, similar to living organisms. Recent neuroscience research has shown that during teleportation in rat navigation, neural populations of place cells in the cornu ammonis-3 region of the hippocampus, which are sparse representations of each other, switch discretely. In this study, we construct a spatial cognition model using brain reference architecture-driven development, a method for developing brain-inspired software that is functionally and structurally consistent with the brain. The spatial cognition model was realized by integrating the recurrent state-space model, a world model, with Monte Carlo localization to infer allocentric self-positions within the framework of neuro-symbol emergence in the robotics toolkit. The spatial cognition model, which models the cornu ammonis-1 and -3 regions with each latent variable, demonstrated improved self-localization performance of mobile robots during teleportation in a simulation environment. Moreover, it was confirmed that sparse neural activity could be obtained for the latent variables corresponding to cornu ammonis-3. These results suggest that spatial cognition models incorporating neuroscience insights can contribute to improving the self-localization technology for mobile robots. The project website is https://nakashimatakeshi.github.io/HF-IGL/.

Delayed formation of neural representations of space in aged mice.

Aging is associated with cognitive deficits, with spatial memory being very susceptible to decline. The hippocampal dentate gyrus (DG) is important for processing spatial information in the brain and is particularly vulnerable to aging, yet its sparse activity has led to difficulties in assessing changes in this area. Using in vivo two-photon calcium imaging, we compared DG neuronal activity and representations of space in young and aged mice walking on an unfamiliar treadmill. We found that calcium activity was significantly higher and less tuned to location in aged mice, resulting in decreased spatial information encoded in the DG. However, with repeated exposure to the same treadmill, both spatial tuning and information levels in aged mice became similar to young mice, while activity remained elevated. Our results show that spatial representations of novel environments are impaired in the aged hippocampus and gradually improve with increased familiarity. Moreover, while the aged DG is hyperexcitable, this does not disrupt neural representations of familiar environments.

Warm Core and Deep Convection in Medicanes: A Passive Microwave-Based Investigation

Mediterranean hurricanes (Medicanes) are characterized by the presence of a quasi-cloud-free calm eye, spiral-like cloud bands, and strong winds around the vortex center. Typically, they reach a tropical-like cyclone (TLC) phase characterized by an axisymmetric warm core without frontal structures. Yet, some of them are not fully symmetrical, have a shallow warm-core structure, and a weak frontal activity. Finding a clear definition and potential classification of Medicanes based on their initiation and intensification processes, understanding the role of convection, and identifying the evolution to a TLC phase are all current research topics. In this study, passive microwave (PMW) measurements and products are used to characterize warm core (WC) and deep convection (DC) for six Medicanes that occurred between 2014 and 2021. A well-established methodology for tropical cyclones, based on PMW temperature sounding channels, is used to identify the WC while PMW diagnostic tools and products (e.g., cloud-top height (CTH) and ice water path (IWP)), combined with lightning data, are used for DC detection and characterization. The application of this methodology to Medicanes highlights the possibility to describe their WC depth, intensity, and symmetry and to identify the cyclone center. We also analyze to what extent the occurrence and characteristics of the WC are related to the Medicane’s intensity and DC development. The results show that Medicanes reaching full TLC phase are associated with deep and symmetric WCs, and that asymmetric DC features in the proximity of the center, and in higher CTH and IWP values, with scarce lighting activity. Medicanes that never develop to a fully TLC structure are associated with a shallower WC, weaker and more sparse DC activity, and lower CTHs and IWP values. Ultimately, this study illustrates the potential of PMW radiometry in providing insights into dynamic and thermodynamic processes associated with Medicanes’ WC characteristics and evolution to TLCs, thus contributing to the ongoing discussion about Medicanes’ definition.

In vitro activities of omadacycline, eravacycline, cefiderocol, apramycin, and comparator antibiotics against Acinetobacter baumannii causing bloodstream infections in Greece, 2020–2021: a multicenter study

Resistance of Acinetobacter baumannii to multiple clinically important antimicrobials has increased to very high rates in Greece, rendering most of them obsolete. The aim of this study was to determine the molecular epidemiology and susceptibilities of A. baumannii isolates collected from different hospitals across Greece. Single-patient A. baumannii strains isolated from blood cultures (n = 271), from 19 hospitals, in a 6-month period (November 2020–April 2021) were subjected to minimum inhibitory concentration determination and molecular testing for carbapenemase, 16S rRNA methyltransferase and mcr gene detection and epidemiological evaluation. 98.9% of all isolates produced carbapenemase OXA-23. The vast majority (91.8%) of OXA-23 producers harbored the armA and were assigned mainly (94.3%) to sequence group G1, corresponding to IC II. Apramycin (EBL-1003) was the most active agent inhibiting 100% of the isolates at ≤16 mg/L, followed by cefiderocol which was active against at least 86% of them. Minocycline, colistin and ampicillin-sulbactam exhibited only sparse activity (S <19%), while eravacycline was 8- and 2-fold more active than minocycline and tigecycline respectively, by comparison of their MIC50/90 values. OXA-23-ArmA producing A. baumannii of international clone II appears to be the prevailing epidemiological type of this organism in Greece. Cefiderocol could provide a useful alternative for difficult to treat Gram-negative infections, while apramycin (EBL-1003), the structurally unique aminoglycoside currently in clinical development, may represent a highly promising agent against multi-drug resistant A. baumanni infections, due to its high susceptibility rates and low toxicity.

EEG-based source localization with enhanced virtual aperture using second order statistics

For the past few decades source localization, based on EEG modality, has been a very active area of research. EEG signal provides temporal resolution in millisecond range that can capture rapidly changing patterns of brain activity but it has a low spatial resolution as compared to techniques like fMRI, PET, CT scan, etc. So, one of the motives of this research is to improve the spatial resolution of the EEG signal. Many successful attempts have been made to localise the active neural sources using EEG signals with the introduction of techniques like MNE, LORETA, sLORETA, FOCUSS, etc. But these techniques require a large number of electrodes for correct localization of a few sources. This paper aims at providing a new method for the localization of EEG sources with a fewer electrode. This is achieved by exploiting the second-order statistics to enhance the aperture and solve the EEG localization problem. The comparison of the proposed method with the state-of-the-art methods is done by observing the localization error with variation in SNR, number of snapshots (time samples), number of active sources, and number of electrodes. The results show that the proposed method can detect a greater number of sources with fewer electrodes and with higher accuracy as compared to methods available in the literature. Real -time EEG signal during an arithmetic task is considered and the proposed algorithm clearly shows a sparse activity in the frontal region.

Covariance-Free Variational Bayesian Learning for Correlated Block Sparse Signals

We consider the problem of estimating channel in massive machine type communication (mMTC) systems. The sparse device activity in a mMTC system makes the channel block-sparse, with intra-block correlation. Block-sparse Bayesian learning (B-SBL) is a powerful framework for estimating such signals. The existing B-SBL algorithms become computationally expensive for high-dimensional problems, which is common in mMTC systems. This is because of large number of devices in a mMTC system, they invert a large-dimensional matrix to calculate the covariance matrix. To address this problem, we exploit variational Bayesian inference, and design a novel covariance-free variational B-SBL algorithm which inverts multiple small-sized block matrices, instead of inverting a complete big-sized matrix. The complexity is further reduced by avoiding explicit computation of the covariance matrix. The proposed algorithm, instead of performing costly matrix inversions, solves multiple linear systems to calculate an unbiased estimate of the posterior statistics. The proposed algorithm is numerically shown to estimate the mMTC channel with a much lesser complexity, and that too without compromising the reconstruction performance.

Uncovering hidden network architecture from spiking activities using an exact statistical input-output relation of neurons

Identifying network architecture from observed neural activities is crucial in neuroscience studies. A key requirement is knowledge of the statistical input-output relation of single neurons in vivo. By utilizing an exact analytical solution of the spike-timing for leaky integrate-and-fire neurons under noisy inputs balanced near the threshold, we construct a framework that links synaptic type, strength, and spiking nonlinearity with the statistics of neuronal population activity. The framework explains structured pairwise and higher-order interactions of neurons receiving common inputs under different architectures. We compared the theoretical predictions with the activity of monkey and mouse V1 neurons and found that excitatory inputs given to pairs explained the observed sparse activity characterized by strong negative triple-wise interactions, thereby ruling out the alternative explanation by shared inhibition. Moreover, we showed that the strong interactions are a signature of excitatory rather than inhibitory inputs whenever the spontaneous rate is low. We present a guide map of neural interactions that help researchers to specify the hidden neuronal motifs underlying observed interactions found in empirical data.

A linear discriminant analysis model of imbalanced associative learning in the mushroom body compartment

To adapt to their environments, animals learn associations between sensory stimuli and unconditioned stimuli. In invertebrates, olfactory associative learning primarily occurs in the mushroom body, which is segregated into separate compartments. Within each compartment, Kenyon cells (KCs) encoding sparse odor representations project onto mushroom body output neurons (MBONs) whose outputs guide behavior. Associated with each compartment is a dopamine neuron (DAN) that modulates plasticity of the KC-MBON synapses within the compartment. Interestingly, DAN-induced plasticity of the KC-MBON synapse is imbalanced in the sense that it only weakens the synapse and is temporally sparse. We propose a normative mechanistic model of the MBON as a linear discriminant analysis (LDA) classifier that predicts the presence of an unconditioned stimulus (class identity) given a KC odor representation (feature vector). Starting from a principled LDA objective function and under the assumption of temporally sparse DAN activity, we derive an online algorithm which maps onto the mushroom body compartment. Our model accounts for the imbalanced learning at the KC-MBON synapse and makes testable predictions that provide clear contrasts with existing models.