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Results for “neural circuits”

Search indexed bioRxiv preprints in genomics, neuroscience, cell biology and bioinformatics. Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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At least 325 records · Page 18Linked to original sources

bioRxiv2018

Dual-color volumetric imaging of neural activity of cortical columns

To capture the emergent properties of neural circuits, high-speed volumetric imaging of neural activity at cellular resolution is desirable. But while conventional two-photon calcium imaging is a powerful tool to study population activity in vivo, it is restrained to two-dimensional planes. Expanding it to 3D while maintaining high spatiotemporal resolution appears necessary. Here, we developed a two-photon microscope with dual-color laser excitation that can image neural activity in a 3D volume. We imaged the neuronal activity of primary visual cortex from awake mice, spanning from L2 to L5 with 10 planes, at a rate of 10 vol/sec, and demonstrated volumetric imaging of L1 long-range PFC projections and L2/3 somatas. Using this method, we map visually-evoked neuronal ensembles in 3D, finding a lack of columnar structure in orientation responses and revealing functional correlations between cortical layers which differ from trial to trial and are missed in sequential imaging. We also reveal functional interactions between presynaptic L1 axons and postsynaptic L2/3 neurons. Volumetric two-photon imaging appears an ideal method for functional connectomics of neural circuits.

neuroscience↗

bioRxiv2019

Zebrafish Dscaml1 is Essential for Retinal Patterning and Function of Oculomotor Subcircuits

Down Syndrome Cell Adhesion Molecules (dscam and dscaml1) are essential regulators of neural circuit assembly, but their roles in vertebrate neural circuit function are still mostly unexplored. We investigated the role of dscaml1 in the zebrafish oculomotor system, where behavior, circuit function, and neuronal activity can be precisely quantified. Loss of zebrafish dscaml1 resulted in deficits in retinal patterning and light adaptation, consistent with its known roles in mammals. Oculomotor analyses showed that mutants have abnormal gaze stabilization, impaired fixation, disconjugation, and faster fatigue. Notably, the saccade and fatigue phenotypes in dscaml1 mutants are reminiscent of human ocular motor apraxia, for which no animal model exists. Two-photon calcium imaging showed that loss of dscaml1 leads to impairment in the saccadic premotor pathway but not the pretectum-vestibular premotor pathway, indicating a subcircuit requirement for dscaml1. Together, we show that dscaml1 has both broad and specific roles in oculomotor circuit function, providing a new animal model to investigate the development of premotor pathways and their associated human ocular disorders.

neuroscience↗

bioRxiv2021

Multiscale modeling of presynaptic dynamics from molecular to mesoscale

Chemical synapses exhibit a diverse array of internal mechanisms that affect the dynamics of transmission efficacy. Many of these processes, such as release of neurotransmitter and vesicle recycling, depend strongly on activity-dependent influx and accumulation of Ca2+. To model how each of these processes may affect the processing of information in neural circuits, and how their dysfunction may lead to disease states, requires a computationally efficient modelling framework, capable of generating accurate phenomenology without incurring a heavy computational cost per synapse. Constructing a phenomenologically realistic model requires the precise characterization of the timing and probability of neurotransmitter release. Difficulties arise in that functional forms of instantaneous release rate can be difficult to extract from noisy data without running many thousands of trials, and in biophysical synapses, facilitation of per-vesicle release probability is confounded by depletion. To overcome this, we obtained traces of free Ca2+ concentration in response to various action potential stimulus trains from a molecular MCell model of a hippocampal mossy fiber axon. Ca2+ sensors were placed at varying distance from a voltage-dependent calcium channel (VDCC) cluster, and Ca2+ was buffered by calbindin. Then, using the calcium traces to drive deterministic state vector models of synaptotagmin 1 and 7 (Syt-1/7), which respectively mediate synchronous and asynchronous release in excitatory hippocampal synapses, we obtained high-resolution profiles of instantaneous release rate, to which we applied functional fits. Synchronous vesicle release occurred predominantly within half a micron of the source of spike-evoked Ca2+ influx, while asynchronous release occurred more consistently at all distances. Both fast and slow mechanisms exhibited multi-exponential release rate curves, whose magnitudes decayed exponentially with distance from the Ca2+ source. Profile parameters facilitate on different time scales according to a single, general facilitation function. These functional descriptions lay the groundwork for efficient mesoscale modelling of vesicular release dynamics. Author SummaryMost information transmission between neurons in the brain occurs via release of neurotransmitter from synaptic vesicles. In response to a presynaptic spike, calcium influx at the active zone of a synapse can trigger the release of neurotransmitter with a certain probability. These stochastic release events may occur immediately after a spike or with some delay. As calcium accumulates from one spike to the next, the probability of release may increase (facilitate) for subsequent spikes. This process, known as short-term plasticity, transforms the spiking code to a release code, underlying much of the brains information processing. In this paper, we use an accurate, detailed model of presynaptic molecular physiology to characterize these processes at high precision in response to various spike trains. We then apply model reduction to the results to obtain a phenomenological model of release timing, probability, and facilitation, which can perform as accurately as the molecular model but with far less computational cost. This mesoscale model of spike-evoked release and facilitation helps to bridge the gap between microscale molecular dynamics and macroscale information processing in neural circuits. It can thus benefit large scale modelling of neural circuits, biologically inspired machine learning models, and the design of neuromorphic chips.

neuroscience↗

bioRxiv2024

Building a small brain with a simple stochastic generative model

The architectures of biological neural networks result from developmental processes shaped by genetically encoded rules, biophysical constraints, stochasticity, and learning. Understanding these processes is crucial for comprehending neural circuits structure and function. The ability to reconstruct neural circuits, and even entire nervous systems, at the neuron and synapse level, facilitates the study of the design principles of neural systems and their developmental plan. Here, we investigate the developing connectome of C. elegans using statistical generative models based on simple biological features: neuronal cell type, neuron birth time, cell body distance, reciprocity, and synaptic pruning. Our models accurately predict synapse existence, degree profiles of individual neurons, and statistics of small network motifs. Importantly, these models require a surprisingly small number of neuronal cell types, which we infer and characterize. We further show that to replicate the experimentally-observed developmental path, multiple developmental epochs are necessary. Validation of our models predictions of the synaptic connections using multiple reconstructions of adult worms suggests that our model identified the fundamental "backbone" of the connectivity graph. The accuracy of the generative statistical models we use here offers a general framework for studying how connectomes develop and the underlying principles of their design.

neuroscience↗

bioRxiv2019

Targeted photostimulation uncovers circuit motifs supporting short-term memory

Short-term memory is associated with persistent neural activity without sustained input, arising from the interactions between neurons with short time constants1,2. A variety of neural circuit motifs could account for measured neural activity3-7. A mechanistic understanding of the neural circuits supporting short-term memory requires probing network connectivity between functionally characterized neurons8. We performed targeted photostimulation of small (< 10) groups of neurons, while imaging the response of hundreds of other neurons9,10, in anterior-lateral motor cortex (ALM) of mice performing a delayed response task11. Mice were instructed with brief auditory stimuli to make directional movements (lick left or lick right), but only after a three second delay epoch. ALM contains neurons with delay epoch activity that is selective for left or right choices. Targeted photostimulation of groups of neurons during the delay epoch allowed us to observe the functional organization of population activity and recurrent interactions underlying short-term memory. These experiments revealed strong coupling between neurons sharing similar selectivity. Brief photostimulation of functionally related neurons produced changes in activity in sparse subpopulations of nearby neurons that persisted for several seconds following stimulus offset, far outlasting the duration of the perturbation. Photostimulation produced behavioral biases that were predictable based on the selectivity of the perturbed neuronal population. These results suggest that ALM contains multiple intercalated modules, consisting of recurrently coupled neurons, that can independently maintain persistent activity.

neuroscience↗

bioRxiv2023

Balance of Activity during a Critical Period Tunes a Developing Network

Developing neural circuits are influenced by activity and are especially sensitive to changes in activity during critical periods (CPs) of development. Changes occurring during a CP often become locked-in so that they affect the mature network. Indeed, several neurodevelopmental disorders have been linked to excessive activity during such periods. It is, therefore, important to identify those aspects of neural circuit development that are influenced by neural activity during a CP. In this study, we take advantage of the genetic tractability of Drosophila to show that activity perturbation during an embryonic CP permanently alters properties of the locomotor circuit. Specific changes we identify include increased synchronicity of motoneuron activity, and greater strengthening of excitatory over inhibitory synaptic drive to motoneurons. These changes are sufficient to reduce network robustness, evidenced by increased sensitivity to induced seizure. We also show that we can rescue these changes when increased activity is mitigated by inhibition provided by mechanosensory neurons. Similarly, we demonstrate a dose-dependent relationship between inhibition experienced during the CP, and the extent to which it is possible to rescue the hyperexcitable phenotype characteristic of the parabss mutation. This suggests that developing circuits must be exposed to a properly balanced sum of excitation and inhibition during the CP to achieve normal mature network function. Our results, therefore, provide novel insight into how activity during a CP shapes specific elements of a circuit, and how activity during this period is integrated to tune neural circuits to the environment in which they will likely function.

neuroscience↗

bioRxiv2020

Activation of central 5HT2A receptor in brain inhibited the seizure-induced respiratory arrest in the DBA/1 mouse SUDEP model

Sudden unexpected death in epilepsy (SUDEP) is the leading cause of death among epilepsy patients, occurring even more frequently in cases with anti-epileptic drug resistance. However, the underlying mechanism of SUDEP remains elusive. Our previous study demonstrated that enhancement of serotonin (5-HT) synthesis by intraperitoneal (IP) injection of 5-hydroxytryptophan (5-HTP) significantly reduced the incidence of seizure-induced respiratory arrest (S-IRA) in a DBA/1 mouse SUDEP model. Given that the 5-HT2A receptor (5-HT2AR) plays an important role in mediating the respiration system in the brain, we hypothesized that 5-HT2AR plays a key role in S-IRA and SUDEP. To test this hypothesis, we examined whether the decreased incidence of S-IRA evoked by either acoustic stimulation or pentylenetetrazole (PTZ) injection following 5-HTP administration will be blocked by treatment with ketanserin (KET), a selective antagonist of 5HT2AR, in the DBA/1 mouse SUDEP model. We observed that the reduction in S-IRA by 5-HTP was significantly reversed by IP or intracerebroventricular injection of KET. Considering the localization of 5-HT2AR in the pre-Botzinger complex (PBC), which plays a key role in regulating respiratory rhythm, we next examined whether KET acts on 5-HT2AR in the PBC. To test this hypothesis, we activated the neural circuit between the dorsal raphe nucleus (DR) and PBC using optogenetics technology. We observed that stimulation of TPH2-ChETA-expressing neurons in the DR reduced the incidence of S-IRA evoked by PTZ, and this suppressant effect was significantly reversed by administration of KET in the bilateral PBC with no changes in electroencephalogram activity. The neural circuit between the DR and PBC was confirmed by injection of cholera toxin subunit B555 (CTB-555), a nerve tracer, in the DR or PBC separately. Calcium signaling evoked by PTZ within neurons of the PBC during seizures was significantly reduced by photostimulation of the DR. Taken together, our findings suggest that 5-HT2AR plays a critical role in regulating S-IRA and targeting the serotonergic neural circuit between the DR and PBC is a promising approach to preventing SUDEP.

neuroscience↗

bioRxiv2023

Ignorance is bliss: effects of noise and metaboliccost on cortical task representations

Cognitive flexibility requires both the encoding of task-relevant and the ignoring of task-irrelevant stimuli. While the neural coding of task-relevant stimuli is increasingly well understood, the mechanisms for ignoring task-irrelevant stimuli remain poorly understood. Here, we study how task performance and biological constraints jointly determine the coding of relevant and irrelevant stimuli in neural circuits. Using mathematical analyses and task-optimized recurrent neural networks, we show that neural circuits can exhibit a range of representational geometries depending on the strength of neural noise and metabolic cost. By comparing these results with recordings from primate prefrontal cortex (PFC) over the course of learning, we show that neural activity in PFC changes in line with a minimal representational strategy. Specifically, our analyses reveal that the suppression of dynamically irrelevant stimuli is achieved by activity-silent, sub-threshold dynamics. Our results provide a normative explanation as to why PFC implements an adaptive, minimal representational strategy.

neuroscience↗

bioRxiv2019

Learning to learn persistently modifies a neocortical-hippocampal excitatory-inhibitory subcircuit

Cognitive control, the judicious use of relevant information while ignoring distractions, is a feature of everyday cognitive experience, but its neurobiology is understudied. We investigated whether cognitive control training (CCT) changes hippocampal neural circuit function in mice, beyond the changes caused by place learning and memory formation. Mice learned and remembered a conditioned place avoidance during CCT that required ignoring irrelevant locations of shock. They were compared to controls that learned the same place avoidance under lower cognitive control demands. Weeks after CCT, mice learn new tasks in novel environments faster than controls; they learned to learn. We investigated entorhinal cortex-to-dentate gyrus neural circuit changes and report that CCT rapidly changes synaptic circuit function, resulting in an excitatory-inhibitory subcircuit change that persists for months. CCT increases inhibition that attenuates the dentate response to medial entorhinal cortical input, and through disinhibition, potentiates the response to strong inputs, pointing to overall signal-to-noise enhancement. These neurobiological findings support a neuroplasticity hypothesis that, beyond storing item/event associations, CCT persistently optimizes neural circuit information processing.

neuroscience↗

bioRxiv2022

bII-spectrin is required for synaptic positioning during retinal development

Neural circuit assembly is a multi-step process where synaptic partners are often born at distinct developmental stages, and yet they must find each other and form precise synaptic connections with one another. This developmental process often relies on late-born neurons extending their processes to the appropriate layer to find and make synaptic connections to their early-born targets. The molecular mechanism responsible for the integration of late-born neurons into an emerging neural circuit remains unclear. Here we uncovered a new role for the cytoskeletal protein {beta}II-spectrin in properly positioning pre- and post-synaptic neurons to the developing synaptic layer. Loss of {beta}II-spectrin disrupts retinal lamination, leads to synaptic connectivity defects, and results in impaired visual function. Together, these findings highlight a new function of {beta}II-spectrin in assembling neural circuits in the mouse outer retina. HighlightsO_LIEstablished a new role for {beta}II-spectrin in assembling retinal circuits C_LIO_LI{beta}II-spectrin positions pre- and post-synaptic neurons to the developing synaptic layer C_LIO_LIEarly positioning of processes to the OPL is required for synaptogenesis C_LIO_LILoss of {beta}II-spectrin disrupts synaptic connectivity and impairs visual function C_LI

neuroscience↗

bioRxiv2021

Offline memory replay in recurrent neuronal networks emerges from constraints on online dynamics

During spatial exploration, neural circuits in the hippocampus store memories of sequences of sensory events encountered in the environment. When sensory information is absent during "offline" resting periods, brief neuronal population bursts can "replay" sequences of activity that resemble bouts of sensory experience. These sequences can occur in either forward or reverse order, and can even include spatial trajectories that have not been experienced, but are consistent with the topology of the environment. The neural circuit mechanisms underlying this variable and flexible sequence generation are unknown. Here we demonstrate in a recurrent spiking network model of hippocampal area CA3 that experimental constraints on network dynamics such as population sparsity, stimulus selectivity, rhythmicity, and spike rate adaptation enable additional emergent properties, including variable offline memory replay. In an online stimulus-driven state, we observed the emergence of neuronal sequences that swept from representations of past to future stimuli on the timescale of the theta rhythm. In an offline state driven only by noise, the network generated both forward and reverse neuronal sequences, and recapitulated the experimental observation that offline memory replay events tend to include salient locations like the site of a reward. These results demonstrate that biological constraints on the dynamics of recurrent neural circuits are sufficient to enable memories of sensory events stored in the strengths of synaptic connections to be flexibly read out during rest and sleep, which is thought to be important for memory consolidation and planning of future behavior.

neuroscience↗

bioRxiv2023

Brain-wide mapping of neuronal architecture controlling torpor

Endotherms can survive low temperatures and food shortage by actively entering a hypometabolic state known as torpor. Although the decrease in metabolic rate and body temperature during torpor is controlled by the brain, the underlying neural circuits remain largely unknown. In this study, we identify the neural circuits involved in torpor regulation by combining whole-brain mapping of torpor-activated neurons, cell type-specific manipulation of neural activity, and viral tracing-based circuit mapping. We found that Trpm2-positive neurons in the preoptic area and Vgat-positive neurons in the dorsal medial hypothalamus are activated during torpor. Genetic silencing shows the activity of either cell type is necessary to enter the torpor state. Finally, we show that these cells receive projections from the arcuate and suprachiasmatic nucleus and send projections to brain regions involved in thermoregulation. Our results demonstrate an essential role of hypothalamic neurons in the regulation of body temperature and metabolic rate during torpor and identify critical nodes of the torpor-regulatory network.

neuroscience↗

bioRxiv2025

Light-field deep learning enables high-throughput, scattering-mitigated calcium imaging

Light field microscopy (LFM) enables volumetric, high throughput functional imaging. However, the computational burden and vulnerability to scattering limit LFMs application to neuroscience. We present a light-field strategy for volumetric, scattering-mitigated neural circuit activity monitoring. A physics-based deep neural network, LNet, is trained with two-photon volumes and one-photon light fields. A processing pipeline uses LNet to extract calcium activity from light-field videos of jGCaMP8f-expressing neurons in acute cortical slices. The extracted time series have high signal-to-noise ratios and reduced optical crosstalk compared to conventional volume reconstruction methods. Imaging 100 volumes per second, we observe putative spikes fired at up to 10 Hz and the spatial intermingling of putative ensembles throughout 530 x 530 x 100-micron volumes. Compared to iterative algorithms, LNet workflows reduce light-field video processing times by 2- to 12-fold, advancing the goal of real-time, scattering-robust volumetric neural circuit imaging for closed-loop and adaptive experimental paradigms.

bioengineering↗

bioRxiv2018

A Neural Microcircuit Model for a Scalable Scale-invariant Representation of Time

Scale-invariant timing has been observed in a wide range of behavioral experiments. The firing properties of recently described time cells provide a possible neural substrate for scale-invariant behavior. Earlier neural circuit models do not produce scale-invariant neural sequences. In this paper we present a biologically detailed network model based on an earlier mathematical algorithm. The simulations incorporate exponentially decaying persistent firing maintained by the calcium-activated nonspecific (CAN) cationic current and a network structure given by the inverse Laplace transform to generate time cells with scale-invariant firing rates. This model provides the first biologically detailed neural circuit for generating scale-invariant time cells. The circuit that implements the inverse Laplace transform merely consists of off-center/on-surround receptive fields. Critically, rescaling temporal sequences can be accomplished simply via cortical gain control (changing the slope of the f-I curve).

neuroscience↗

bioRxiv2020

Developmental and evolutionary constraints on olfactory circuit selection

Across species, neural circuits show remarkable regularity, suggesting that their structure has been driven by underlying optimality principles. Here, we ask whether we can predict the neural circuitry of diverse species by optimizing the neural architecture to make learning as efficient as possible. We focus on the olfactory system, primarily because it has a relatively simple evolutionarily conserved structure, and because its input and intermediate layer sizes exhibits a tight allometric scaling. In mammals, it has been shown that the number of neurons in layer 2 of piriform cortex scales as the number of glomeruli (the input units) to the 3/2 power; in invertebrates, we show that the number of mushroom body Kenyon cells scales as the number of glomeruli to the 7/2 power. To understand these scaling laws, we model the olfactory system as a three layered nonlinear neural network, and analytically optimize the intermediate layer size for efficient learning from a limited number of samples. We find that the 3/2 scaling observed in mammals emerges naturally, both in full batch optimization and under stochastic gradient learning. We extended the framework to the case where a fraction of the olfactory circuit is genetically specified, not learned. We show numerically that this makes the scaling law steeper when the number of glomeruli is small, and we are able to recover the 7/2 scaling law observed in invertebrates. This study paves the way for a deeper understanding of the organization of brain circuits from an evolutionary perspective.

neuroscience↗

bioRxiv2021

The cytokine receptor Fn14 regulates neuronal transcription during development and brain function in the adult

Cytokine signaling pathways that promote inflammation in peripheral tissues are repurposed to coordinate the refinement of synaptic connections in the developing brain. However, the downstream mechanisms through which these pathways mediate neural circuit maturation remain to be fully defined. Here, we demonstrate that Fn14, a cytokine receptor that promotes inflammation outside of the central nervous system, shapes the transcriptional profiles and chromatin landscapes of neurons in the developing brain. Single-nucleus RNA-sequencing revealed hundreds of misregulated genes in the thalamocortical neurons of the visual thalami of mice lacking either Fn14 or its microglial derived cytokine ligand TWEAK, including genes encoding proteins with critical roles in synaptic function, histone modification, and chromatin remodeling. Whole-genome analysis uncovered significant alterations in chromatin accessibility in the brains of mice lacking Fn14 or in wild-type mice following microglial depletion, and chromatin changes due to both manipulations were enriched near genes encoding regulators of synaptic function. Loss of microglia also led to the retention of excess synapses, suggesting that microglia may link modifications in neuronal chromatin to the functional refinement of neural circuits. Consistent with Fn14 shaping brain function beyond the visual system, Fn14 knockout mice displayed impairments in memory task proficiency as well as heightened sensitivity to pharmacologically induced seizures. Taken together, these results define a previously undescribed interaction between microglia, cytokine signaling, and the neuronal epigenome that is likely to contribute to neural circuit refinement and function in the brain.

neuroscience↗

bioRxiv2018

Optogenetic manipulation of medullary neurons in the locust optic lobe

Locust is a widely used animal model for studying sensory processing and its relation to behavior. Due to the lack of genomic information, genetic tools to manipulate neural circuits in locusts are not yet available. We examined whether Semliki Forest virus is suitable to mediate exogenous gene expression in neurons of the locust optic lobe. We subcloned a channelrhodopsin variant and the yellow fluorescent protein Venus into a Semliki Forest virus vector and injected the virus into the optic lobe of locusts (Schistocerca americana). Fluorescence was observed in all injected optic lobes. Most neurons that expressed the recombinant proteins were located in the first two neuropils of the optic lobe, the lamina and medulla. Extracellular recordings demonstrated that laser illumination increased the firing rate of medullary neurons expressing channelrhodopsin. The optogenetic activation of the medullary neurons also triggered firing of a postsynaptic, looming-sensitive neuron, the Lobula Giant Movement Detector (LGMD). These results indicate that Semliki Forest virus is efficient at mediating transient exogenous gene expression and provides a tool to manipulate neural circuits in the locust nervous system and likely other insects.\n\nNew and NoteworthyUsing Semliki Forest virus, we efficiently delivered channelrhodopsin into neurons of the locust optic lobe. We demonstrate that laser illumination increases the firing of the medullary neurons expressing channelrhodopsin and of an identified postsynaptic target neuron, the LGMD neuron. This technique allows to manipulate the neuronal activity in locust neural circuits using optogenetics.

neuroscience↗

bioRxiv2025

Local homeostatic scaling supports stable rate propagation under noise and heterogeneity

Reliable transmission of firing-rate signals through neural circuits requires synaptic coupling to remain within a narrow regime shaped by neuronal heterogeneity and noise. Outside this regime, classical theory predicts that activity will dissipate or amplify into synchrony. Studies have shown that stable rate-code transmission is biologically achievable, but they leave open how the required operating regime is established. Canonical plasticity rules including Hebbian plasticity and conventional homeostatic scaling do not by themselves recover this regime. We therefore introduce relational homeostatic scaling, a local synaptic scaling rule in which each postsynaptic neuron adjusts its total excitatory afferent strength to reduce the discrepancy between a recent AMPA-weighted afferent activity trace and a recent postsynaptic activity trace. Together, these traces capture the recent rate entering and leaving the neuron without requiring a global rate target. Mean-field analysis and simulations show that this rule drives synaptic weights toward the critical regime without precise weight initialization. The rule remains robust to neuronal heterogeneity, composes with spike-timing-dependent plasticity without disrupting Hebbian competition, and stabilizes the eligibility trace required by three-factor learning rules. These findings suggest that relational homeostatic scaling may provide a substrate for stable rate-code transmission in neural circuits. Author SummaryNeural signals often pass through many processing stages, but this is difficult in networks made of noisy and heterogeneous neurons. Classical feedforward models predict that activity will either fade away or become too synchronized unless synaptic strengths fall within a narrow stable regime. Recent studies have shown that biological neural networks can transmit firing-rate information reliably. How do neural circuits find and maintain the operating regime needed for stable rate-code transmission? We propose a simple local mechanism, relational homeostatic scaling, in which each neuron adjusts the strength of its incoming excitatory synapses by comparing a recent trace of its input activity with a trace of its own activity. Unlike conventional homeostatic scaling, the goal is not to force every neuron toward a fixed firing rate. The goal is to preserve the relationship between input drive and output activity. We show that this rule allows layered spiking networks to self-organize into the stable transmission regime, remains robust to neuronal heterogeneity, and works alongside Hebbian spike-timing plasticity. These results suggest that homeostatic plasticity may help establish the circuit conditions under which experimentally observed rate-code transmission can occur.

neuroscience↗