Synaptic Delay Research Articles

Oligosynaptic inhibition of group I afferents between the brachioradialis and flexor carpi radialis in humans

Spinal reflex arcs mediated by low threshold afferents between the brachioradialis (BR) and flexor carpi radialis (FCR) were studied in eleven healthy human subjects using a post-stimulus time-histogram method. Electrical conditioning stimuli (ES) to the radial nerve branch innervating BR with the intensity below the motor threshold (MT) induced an early and significant trough (inhibition) in 32/85 FCR motor units (MUs) in 9/9 subjects. Such inhibition was never provoked by cutaneous stimulation. The central synaptic delay (CSD) of the inhibition was approximately 1.1ms longer than that of the homonymous FCR facilitation. ES to the median nerve branch innervating FCR with the intensity below MT induced an inhibition in 27/71 BR-MUs in 10/10 subjects. CSD of the inhibition was about 1.1ms longer than that of the homonymous BR facilitation. These findings suggest that inhibition between BR and FCR exists in humans. Group I afferents seem to mediate the inhibition through an oligo(di or tri)-synaptic path.

Effects of Deltamethrin on crayfish motor axon activity and neuromuscular transmission

Deltamethrin (DM) is a widely used pesticide known to target sodium channels. Although this compound has been studied extensively at molecular and behavioral levels, the detailed action of DM on cellular and synaptic function is less well documented. In this report, we show that DM at nanomolar concentrations can silence tonic motor output of the crayfish ventral superficial flexor (VSF) within ∼10min. Action potential (AP) amplitude was consistently reduced before silencing occurred, whereas AP duration and AP firing frequency did not change. In some synapses EPSP amplitude and synaptic delay were modified by DM, but the direction of change was not consistent. In order to better understand these diverse effects, intracellular recordings from motor axons of the crayfish opener were used for a detailed analysis. DM caused an initial, slow depolarization of resting membrane potential (Vm), which was accompanied by reduced AP amplitude but not AP duration. Resting Vm then underwent a step depolarization of ∼20mV, which we propose corresponds to the onset of the depolarization block. In addition, DM shifted the AP initiation site in some opener axons during prolonged firing. This shift occurred concomitantly with a reduction in synaptic delay. A similar reduction in synaptic delay was also detected at some VSF axons, and can be attributed to the same mechanism. Results reported here suggest that DM at low concentrations result in: (i) depolarization block of motor axons before changes in network output can be detected, (ii) variable effects on synaptic transmission, with this variability presumably due to the diverse morphology and excitability of motor axons.

Rich-Club Organization in Effective Connectivity among Cortical Neurons.

The performance of complex networks, like the brain, depends on how effectively their elements communicate. Despite the importance of communication, it is virtually unknown how information is transferred in local cortical networks, consisting of hundreds of closely spaced neurons. To address this, it is important to record simultaneously from hundreds of neurons at a spacing that matches typical axonal connection distances, and at a temporal resolution that matches synaptic delays. We used a 512-electrode array (60 μm spacing) to record spontaneous activity at 20 kHz from up to 500 neurons simultaneously in slice cultures of mouse somatosensory cortex for 1 h at a time. We applied a previously validated version of transfer entropy to quantify information transfer. Similar to in vivo reports, we found an approximately lognormal distribution of firing rates. Pairwise information transfer strengths also were nearly lognormally distributed, similar to reports of synaptic strengths. Some neurons transferred and received much more information than others, which is consistent with previous predictions. Neurons with the highest outgoing and incoming information transfer were more strongly connected to each other than chance, thus forming a "rich club." We found similar results in networks recorded in vivo from rodent cortex, suggesting the generality of these findings. A rich-club structure has been found previously in large-scale human brain networks and is thought to facilitate communication between cortical regions. The discovery of a small, but information-rich, subset of neurons within cortical regions suggests that this population will play a vital role in communication, learning, and memory. Significance statement: Many studies have focused on communication networks between cortical brain regions. In contrast, very few studies have examined communication networks within a cortical region. This is the first study to combine such a large number of neurons (several hundred at a time) with such high temporal resolution (so we can know the direction of communication between neurons) for mapping networks within cortex. We found that information was not transferred equally through all neurons. Instead, ∼70% of the information passed through only 20% of the neurons. Network models suggest that this highly concentrated pattern of information transfer would be both efficient and robust to damage. Therefore, this work may help in understanding how the cortex processes information and responds to neurodegenerative diseases.

Simulation of Spike Wave Propagation and Two-to-one Communication with Dynamic Time Warping

Although intercommunication among the different areas of the brain is well known, the rules of communication in the brain are not clear. Many previous studies have examined the firing patterns of neural networks in general, while we have examined the involvement of the firing patterns of neural networks in communication. In order to understand information processing in the brain, we simulated the interactions of the firing activities of a large number of neural networks in a 25 × 25 two-dimensional array for analyzing spike behavior. We stimulated the transmitting neurons at 0.1 msec. Then we observed the generated spike propagation for 120 msec. In addition, the positions of the firing neurons were determined with spike waves for different variances in the temporal fluctuations of the neuronal characteristics. These results suggested that for the changes (diversity) in the propagation routes of neuronal transmission resulted from variance in synaptic propagation delays and refractory periods. The simulation was used to examine differences in the percentages of neurons with significantly larger test statistics and the variances in the synaptic delay and refractory period. These results suggested that multiplex communication was more stable if the synaptic delay and refractory period varied.

Rhythmic Oscillations of Excitatory Bursting Hodkin-Huxley Neuronal Network with Synaptic Learning.

Rhythmic oscillations of neuronal network are actually kind of synchronous behaviors, which play an important role in neural systems. In this paper, the properties of excitement degree and oscillation frequency of excitatory bursting Hodkin-Huxley neuronal network which incorporates a synaptic learning rule are studied. The effects of coupling strength, synaptic learning rate, and other parameters of chemical synapses, such as synaptic delay and decay time constant, are explored, respectively. It is found that the increase of the coupling strength can weaken the extent of excitement, whereas increasing the synaptic learning rate makes the network more excited in a certain range; along with the increasing of the delay time and the decay time constant, the excitement degree increases at the beginning, then decreases, and keeps stable. It is also found that, along with the increase of the synaptic learning rate, the coupling strength, the delay time, and the decay time constant, the oscillation frequency of the network decreases monotonically.

Darwin: a neuromorphic hardware co-processor based on Spiking Neural Networks

Broadly speaking, the goal of neuromorphic engineering is to build computer systems that mimic the brain. Spiking Neural Network (SNN) is a type of biologically-inspired neural networks that perform information processing based on discrete-time spikes, different from traditional artificial Neural Network (ANN). Hardware implementation of SNNs is necessary for achieving high-performance and low-power. We present the Darwin Neural Processing Unit (NPU), a neuromorphic hardware co-processor based on sNN implemented with digitallogic, supporting a maximum of 2048 neurons, $2048^{2} = 4194304$ synapses, and 15 possible synaptic delays. The Darwin NPU was fabricated by standard 180 nm CMOS technology with an area size of $5\times 5~\text{mm}^{2}$ and 70 MHz clock frequency at the worst case. It consumes 0.84 mW/MHz with 1.8 V power supply for typical applications. Two prototype applications are used to demonstrate the performance and efficiency of the hardware implementation.

Parallelizing large networks using NEURON-Python

Research on brain organization has become increasingly dependent on large multiscale neuronal network simulations. Here we describe current usage of the NEURON simulator with MPI (message passing interface) in Python for simulation of networks in the high performance computing (HPC) parallel computing environment [1-4]. The mixed ordinary differential equation (ODE) and event-driven nature of neuronal simulations offers advantages for parallelization, by allowing each node to work independently for a period equivalent to the minimal synaptic delay before exchanging queue information with other nodes, obviating the need to exchange information at every time step. NEURON's ParallelContext allows access to a few of the important general collective MPI calls, as well as calls adapted from prior usages from the LINDA package, now reimplemented under MPI. From Python, a NEURON ParallelContext is created using pc = h.ParallelContext(), where h provides access to NEURON simulation objects after from neuron import h, ParallelContext permits the periodic transfer of spike information via queue exchanges. Pseudo-random streams must be consistent regardless of numbers of nodes in order to set connectivity, delays, and weights that are not fully defined from experimental studies. These streams are kept consistent regardless of number of nodes being used and therefore allows for the simulations to be identical. In order to create this, randomizers are established for particular purposes using NEURON's h.Random().Random123().[5] The key to reproducibility is to define each randomizer according to 1. a particular usage, 2. a particular cell (based on a global identifier or gid), 3. a particular run based on a run identifier runid: e.g., after for r in randomizers: r.Random123_globalindex(runid, randomdel.Random123(id32('randomdel'), self.gid, 0) where id32() provides a 32-bit hash for a name: def id32(obj):return hash(obj)&0xffffffff. Data saving must be initially managed at node of origin and then combined across nodes, to be accessible for analysis or viewing. Given that file saving may occur incrementally during simulation from different nodes on different local filesystems, file management becomes important. There are several ways to handle data saving, the benefits and applicability of which will be presented. Spike recordings are created as vectors on a per-node basis, later consolidated. Other state variables may also be saved at the same time, or may be recreated later utilizing a re-run of individual cells with identical stimulation using NEURON's PatternStim. We note that ParallelContext in NEURON permits the development of hybrid networks using various types of cells: event-driven cells, integrate-and-fire cells, multicompartment cells, as well as complex cells with calculation of internal chemical milieu. Load balancing in the hybrid circumstance is a crucial issue, particularly when some cells are computationally large due to inclusion of reaction-diffusion mechanisms to develop multiscale models from molecule to network.

A stochastic mechanism for signal propagation in the brain: Force of rapid random fluctuations in membrane potentials of individual neurons

There are two functionally important factors in signal propagation in a brain structural network: the very first synaptic delay–a time delay about 1ms–from the moment when signals originate to the moment when observation on the signal propagation can begin; and rapid random fluctuations in membrane potentials of every individual neuron in the network at a timescale of microseconds. We provide a stochastic analysis of signal propagation in a general setting. The analysis shows that the two factors together result in a stochastic mechanism for the signal propagation as described below. A brain structural network is not a rigid circuit rather a very flexible framework that guides signals to propagate but does not guarantee success of the signal propagation. In such a framework, with the very first synaptic delay, rapid random fluctuations in every individual neuron in the network cause an “alter-and-concentrate effect” that almost surely forces signals to successfully propagate. By the stochastic mechanism we provide analytic evidence for the existence of a force behind signal propagation in a brain structural network caused by rapid random fluctuations in every individual neuron in the network at a timescale of microseconds with a time delay of 1ms.

Quantitative analysis of human cutaneous vasomotor innervation

Background: A neural recruitment strategy exists within the sympathetic nervous system (SNS) of young individuals, whereby elevated sympathetic outflow during reflex-mediated stress is associated with recruitment of latent sub-populations of largeramplitude axons and acute modifications in synaptic delays. Aim: We investigated whether these recruitment patterns persist with healthy aging and coronary artery disease (CAD). Methods: Muscle sympathetic nerve activity (MSNA; microneurography) was measured in 14 young healthy individuals (YH; 25 ± 4 yrs), 14 older healthy individuals (OH; 59 ± 9 yrs), and 15 CAD patients (63 ± 11 yrs) at baseline and during a maximal end-inspiratory apnea. Action potential (AP) patterns were studied using a novel AP analysis technique. Results: Baseline burst frequency was greater in OH (35 ± 10 bursts/min) and CAD (39 ± 5 bursts/min), compared to YH (17 ± 7 bursts/min; all P b 0.001). APs/min and APs/burst increased during apnea (time effect; all P b 0.001). When APs were binned according to peak-to-peak amplitude (i.e., into ‘clusters’), total ‘clusters’ increased during apnea in YH (Δ6 ± 3; P b 0.001) and OH (Δ 2±1 ; P b 0.01), but was unchanged in CAD (Δ0 ± 1; P N 0.05). The number of active clusters/burst increased during apnea in YH (Δ 2±1 ; P b 0.001), OH (Δ1 ± 1; P b 0.01), and CAD (Δ1 ± 1; P = 0.01) groups, although greater change occurred in YH versus OH and CAD (all P b 0.01). In all groups, a pattern emerged where the cluster size-latency profile was shifted downwards for every corresponding cluster during apnea. Conclusion: The capacity of the SNS to recruit latent sub-populations of larger-amplitude axons is reduced with healthy aging and lost in CAD, while the ability to acutely modify synaptic delays appears unaltered.

Thyroid hormone is required for pruning, functioning and long-term maintenance of afferent inner hair cell synapses.

Functional maturation of afferent synaptic connections to inner hair cells (IHCs) involves pruning of excess synapses formed during development, as well as the strengthening and survival of the retained synapses. These events take place during the thyroid hormone (TH)-critical period of cochlear development, which is in the perinatal period for mice and in the third trimester for humans. Here, we used the hypothyroid Snell dwarf mouse (Pit1(dw)) as a model to study the role of TH in afferent type I synaptic refinement and functional maturation. We observed defects in afferent synaptic pruning and delays in calcium channel clustering in the IHCs of Pit1(dw) mice. Nevertheless, calcium currents and capacitance reached near normal levels in Pit1(dw) IHCs by the age of onset of hearing, despite the excess number of retained synapses. We restored normal synaptic pruning in Pit1(dw) IHCs by supplementing with TH from postnatal day (P)3 to P8, establishing this window as being critical for TH action on this process. Afferent terminals of older Pit1(dw) IHCs showed evidence of excitotoxic damage accompanied by a concomitant reduction in the levels of the glial glutamate transporter, GLAST. Our results indicate that a lack of TH during a critical period of inner ear development causes defects in pruning and long-term homeostatic maintenance of afferent synapses.

A unified framework for spiking and gap-junction interactions in distributed neuronal network simulations.

Contemporary simulators for networks of point and few-compartment model neurons come with a plethora of ready-to-use neuron and synapse models and support complex network topologies. Recent technological advancements have broadened the spectrum of application further to the efficient simulation of brain-scale networks on supercomputers. In distributed network simulations the amount of spike data that accrues per millisecond and process is typically low, such that a common optimization strategy is to communicate spikes at relatively long intervals, where the upper limit is given by the shortest synaptic transmission delay in the network. This approach is well-suited for simulations that employ only chemical synapses but it has so far impeded the incorporation of gap-junction models, which require instantaneous neuronal interactions. Here, we present a numerical algorithm based on a waveform-relaxation technique which allows for network simulations with gap junctions in a way that is compatible with the delayed communication strategy. Using a reference implementation in the NEST simulator, we demonstrate that the algorithm and the required data structures can be smoothly integrated with existing code such that they complement the infrastructure for spiking connections. To show that the unified framework for gap-junction and spiking interactions achieves high performance and delivers high accuracy in the presence of gap junctions, we present benchmarks for workstations, clusters, and supercomputers. Finally, we discuss limitations of the novel technology.

Stability, bifurcations and synchronization in a delayed neural network model of [formula omitted]-identical neurons

This paper deals with dynamic behaviors of Hopfield type neural network model of n(≥3) identical neurons with two time-delayed connections coupled in a star configuration. Delay dependent as well as independent local stability conditions about trivial equilibrium is found. Considering synaptic weight and time delay as parameters Hopf-bifurcation, steady-state bifurcation and equivariant steady state bifurcation criteria are given. The criterion for the global stability of the system is presented by constructing a suitable Lyapunov functional. Also conditions for absolute synchronization about the trivial equilibrium are also shown. Using normal form method and the center manifold theory the direction of the Hopf-bifurcation, stability and the properties of Hopf-bifurcating periodic solutions are determined. Numerical simulations are presented to verify the analytical results. The effect of synaptic weight and delay on different types of oscillations, e.g. in-phase, phase-locking, standing wave and oscillation death, has been shown numerically.

Loss of Local Astrocyte Support Disrupts Action Potential Propagation and Glutamate Release Synchrony from Unmyelinated Hippocampal Axon Terminals In Vitro.

Certain glial cell types (oligodendrocytes, Schwann cells) facilitate the propagation of neuronal electrical signals, but a role for astrocytes has not been identified despite many other functions of astrocytes in supporting and modulating neuronal signaling. Under identical global conditions, we cultured neurons with or without local astrocyte support. Without local astrocytes, glutamate transmission was desynchronized by an alteration of the waveform and arrival time of axonal action potentials to synaptic terminals. GABA transmission was not disrupted. The disruption did not involve detectable morphological changes to axons of glutamate neurons. Our work identifies a developmental role for astrocytes in the temporal precision of excitatory signals.

Bayesian analysis of the kinetics of quantal transmitter secretion at the neuromuscular junction.

The timing of transmitter release from nerve endings is considered nowadays as one of the factors determining the plasticity and efficacy of synaptic transmission. In the neuromuscular junction, the moments of release of individual acetylcholine quanta are related to the synaptic delays of uniquantal endplate currents recorded under conditions of lowered extracellular calcium. Using Bayesian modelling, we performed a statistical analysis of synaptic delays in mouse neuromuscular junction with different patterns of rhythmic nerve stimulation and when the entry of calcium ions into the nerve terminal was modified. We have obtained a statistical model of the release timing which is represented as the summation of two independent statistical distributions. The first of these is the exponentially modified Gaussian distribution. The mixture of normal and exponential components in this distribution can be interpreted as a two-stage mechanism of early and late periods of phasic synchronous secretion. The parameters of this distribution depend on both the stimulation frequency of the motor nerve and the calcium ions' entry conditions. The second distribution was modelled as quasi-uniform, with parameters independent of nerve stimulation frequency and calcium entry. Two different probability density functions for the distribution of synaptic delays suggest at least two independent processes controlling the time course of secretion, one of them potentially involving two stages. The relative contribution of these processes to the total number of mediator quanta released depends differently on the motor nerve stimulation pattern and on calcium ion entry into nerve endings.

Physiologically motivated multiplex Kuramoto model describes phase diagram of cortical activity.

We derive a two-layer multiplex Kuramoto model from Wilson-Cowan type physiological equations that describe neural activity on a network of interconnected cortical regions. This is mathematically possible due to the existence of a unique, stable limit cycle, weak coupling, and inhibitory synaptic time delays. We study the phase diagram of this model numerically as a function of the inter-regional connection strength that is related to cerebral blood flow, and a phase shift parameter that is associated with synaptic GABA concentrations. We find three macroscopic phases of cortical activity: background activity (unsynchronized oscillations), epileptiform activity (highly synchronized oscillations) and resting-state activity (synchronized clusters/chaotic behaviour). Previous network models could hitherto not explain the existence of all three phases. We further observe a shift of the average oscillation frequency towards lower values together with the appearance of coherent slow oscillations at the transition from resting-state to epileptiform activity. This observation is fully in line with experimental data and could explain the influence of GABAergic drugs both on gamma oscillations and epileptic states. Compared to previous models for gamma oscillations and resting-state activity, the multiplex Kuramoto model not only provides a unifying framework, but also has a direct connection to measurable physiological parameters.

Sympathetic neural recruitment strategies: responses to severe chemoreflex and baroreflex stress.

This study tested the hypothesis that neural coding patterns exist within the autonomic nervous system. We investigated sympathetic axonal recruitment strategies in humans during chemoreflex- and baroreflex-mediated sympathoexcitation using a novel action potential (AP) analysis technique. Muscle sympathetic nerve activity (microneurography) was collected in 11 young individuals (6 females) during baseline and two subsequent protocols: 1) severe chemoreflex stimulation (maximal end-inspiratory apnea following rebreathe), and 2) severe baroreceptor unloading (-80 mmHg lower body negative pressure; LBNP). When compared with each respective baseline, apnea and LBNP increased AP frequency and mean AP content per sympathetic burst (all P < 0.01). When APs were binned according to peak-to-peak amplitude (i.e., into "clusters"), total clusters detected increased during both apnea (Δ7 ± 5; P = 0.0009) and LBNP (Δ11 ± 8; P = 0.0012) compared with baseline. This was concomitant to an increased number of active clusters per burst during apnea (Δ3 ± 1; P < 0.0001) and LBNP (Δ3 ± 3; P = 0.0076). At baseline and during apnea (R(2) = 0.98; P < 0.0001) and LBNP (R(2) = 0.95; P < 0.0001), a pattern emerged whereby AP cluster latency decreased as cluster size increased. Furthermore, the AP cluster latency profile was shifted downward during apnea (∼53 ms) and upward during LBNP (∼31 ms). The data indicate that variations in synaptic delays and latent subpopulations of larger axons exist as recruitment strategies for sympathetic outflow. The synaptic delay component appears to express reflex specificity, whereas latent subpopulation recruitment demonstrates sensitivity to stress severity.

Introducing Synaptic Delays in the NEAT Algorithm to Improve Modelling in Cognitive Robotics

This paper describes and tests an approach to improve the temporal processing capabilities of the neuroevolution of augmenting topologies (NEAT) algorithm. This algorithm is quite popular within the robotics community for the production of trained neural networks without having to determine a priori their size and topology. The main drawback of the traditional NEAT algorithm is that, even though it can implement recurrent synaptic connections, which allow it to perform some time related processing tasks, its capabilities are rather limited, especially when dealing with precise time dependent phenomena. NEAT's ability to capture the underlying dynamics that correspond to complex time series still has a lot of room for improvement. To address this issue, the paper describes a new implementation of the NEAT algorithm that is able to generate artificial neural networks (ANNs) with trainable time delayed synapses in addition to its previous capacities. We show that this approach, called $$\uptau $$?-NEAT improves the behavior of the neural networks obtained when dealing with complex time related processes. Several examples are presented, both dealing with the generation of ANNs that are able to produce complex theoretical signals such as chaotic signals or real data series, as in the case of the monthly number of international airline passengers or monthly $$\hbox {CO}_{2}$$CO2 concentrations. In these examples, $$\uptau $$?-NEAT clearly improves over the traditional NEAT algorithm in these tasks. A final example of the integration of this approach within a robot cognitive mechanism is also presented, showing the clear improvements it could provide in the modeling required for many cognitive processes.

Robust Synchronization in an E/I Network with Medium Synaptic Delay and High Level of Heterogeneity**Supported by the National Natural Science Foundation of China under Grant Nos 11102038, 11472061, 70971021, 71371046 and 61203325, the Shanghai Natural Science Foundation under Grant No 13ZR1400200, the Undergraduate Education Key Reform Project of Shanghai Universities under Grant No X12071306, and the Fundamental Research Funds for

It is known that both excitatory and inhibitory neuronal networks can achieve robust synchronization only under certain conditions, such as long synaptic delay or low level of heterogeneity. In this work, robust synchronization can be found in an excitatory/inhibitory (E/I) neuronal network with medium synaptic delay and high level of heterogeneity, which often occurs in real neuronal networks. Two effects of post-synaptic potentials (PSP) to network synchronization are presented, and the synaptic contribution of excitatory and inhibitory neurons to robust synchronization in this E/I network is investigated. It is found that both excitatory and inhibitory neurons may contribute to robust synchronization in E/I networks, especially the excitatory PSP has a more positive effect on synchronization in E/I networks than that in excitatory networks. This may explain the strong robustness of synchronization in E/I neuronal networks.

Inferring synaptic structure in presence of neural interaction time scales.

Biological networks display a variety of activity patterns reflecting a web of interactions that is complex both in space and time. Yet inference methods have mainly focused on reconstructing, from the network’s activity, the spatial structure, by assuming equilibrium conditions or, more recently, a probabilistic dynamics with a single arbitrary time-step. Here we show that, under this latter assumption, the inference procedure fails to reconstruct the synaptic matrix of a network of integrate-and-fire neurons when the chosen time scale of interaction does not closely match the synaptic delay or when no single time scale for the interaction can be identified; such failure, moreover, exposes a distinctive bias of the inference method that can lead to infer as inhibitory the excitatory synapses with interaction time scales longer than the model’s time-step. We therefore introduce a new two-step method, that first infers through cross-correlation profiles the delay-structure of the network and then reconstructs the synaptic matrix, and successfully test it on networks with different topologies and in different activity regimes. Although step one is able to accurately recover the delay-structure of the network, thus getting rid of any a priori guess about the time scales of the interaction, the inference method introduces nonetheless an arbitrary time scale, the time-bin dt used to binarize the spike trains. We therefore analytically and numerically study how the choice of dt affects the inference in our network model, finding that the relationship between the inferred couplings and the real synaptic efficacies, albeit being quadratic in both cases, depends critically on dt for the excitatory synapses only, whilst being basically independent of it for the inhibitory ones.

DL-ReSuMe: A Delay Learning-Based Remote Supervised Method for Spiking Neurons.

Recent research has shown the potential capability of spiking neural networks (SNNs) to model complex information processing in the brain. There is biological evidence to prove the use of the precise timing of spikes for information coding. However, the exact learning mechanism in which the neuron is trained to fire at precise times remains an open problem. The majority of the existing learning methods for SNNs are based on weight adjustment. However, there is also biological evidence that the synaptic delay is not constant. In this paper, a learning method for spiking neurons, called delay learning remote supervised method (DL-ReSuMe), is proposed to merge the delay shift approach and ReSuMe-based weight adjustment to enhance the learning performance. DL-ReSuMe uses more biologically plausible properties, such as delay learning, and needs less weight adjustment than ReSuMe. Simulation results have shown that the proposed DL-ReSuMe approach achieves learning accuracy and learning speed improvements compared with ReSuMe.