Spike Frequency Adaptation Research Articles

Alterations in the intrinsic discharge activity of CA1 pyramidal neurons associated with possible changes in the NADPH diaphorase activity in a rat model of autism induced by prenatal exposure to valproic acid

Autism spectrum disorder is a neurodevelopmental disorder characterized by sensory abnormalities, social skills impairment and cognitive deficits. Although recent evidence indicated that induction of autism-like behavior in animal models causes abnormal neuronal excitability, the impact of autism on neuronal properties is still an important issue. Thus, new findings at the cellular level may shed light on the pathophysiology of autism and may help to find effective treatment strategies. Here, we investigated the behavioral, electrophysiological and histochemical impacts of prenatal exposure to valproic acid (VPA) in rats. Findings revealed that VPA exposure caused a significant increase in the hot plate response latency. The novel object recognition ability was also impaired in VPA-exposed rats. Along with these behavioral alterations, neurons from VPA-exposed animals exhibited altered excitability features in response to depolarizing current injections relative to control neurons. In the VPA-exposed group, these changes consisted of a significant increase in the amplitude, evoked firing frequency and the steady-state standard deviation of spike timing of action potentials (APs). Moreover, the half-width, the AHP amplitude and the decay time constant of APs were significantly decreased in this group. These changes in the evoked electrophysiological properties were accompanied by intrinsic hyperexcitability and lower spike-frequency adaptation and also a significant increase in the number of NADPH-diaphorase stained neurons in the hippocampal CA1 area of the VPA-exposed rats. Taken together, findings demonstrate that abnormal nociception and recognition memory is associated with alterations in the neuronal responsiveness and nitrergic system in a rat model of autism-like.

Theoretical analysis of effects of transcranial magneto-acoustical stimulation on neuronal spike-frequency adaptation

BackgroundTranscranial magneto-acoustical stimulation (TMAS) is a noninvasive technique that has advantages in spatial resolution and penetration depth. It changes the firing properties of neurons through the current generated by focused ultrasound and a static magnetic field. Spike-frequency adaptation is an important dynamic characteristic of neural information processing.MethodsTo address the effects of TMAS on neural spike-frequency adaptation, this study employs some ultrasound and magnetic field parameters, such as magnetic flux density, ultrasonic intensity, fundamental ultrasonic frequency, modulation frequency, and duty cycle. Using these different ultrasound and magnetic field parameters, membrane potential curves, spike-frequency curves, and adapted onset spike-frequency curves are exhibited and analyzed.ResultsThe results show that spike-frequency adaptation is strongly dependent on ultrasonic intensity and magnetic flux density and is rarely affected by other parameters. However, modulation frequency and duty cycle influence membrane potentials and spike frequencies to some degree.ConclusionsThis study reveals the mechanism of the effects of TMAS on neural spike-frequency adaptation and serves as theoretical guidance for TMAS experiments.

Slow Inactivation of Sodium Channels Contributes to Short-Term Adaptation in Vomeronasal Sensory Neurons.

Adaptation plays an important role in sensory systems as it dynamically modifies sensitivity to allow the detection of stimulus changes. The vomeronasal system controls many social behaviors in most mammals by detecting pheromones released by conspecifics. Stimuli activate a transduction cascade in vomeronasal neurons that leads to spiking activity. Whether and how these neurons adapt to stimuli is still debated and largely unknown. Here, we measured short-term adaptation performing current-clamp whole-cell recordings by using diluted urine as a stimulus, as it contains many pheromones. We measured spike frequency adaptation in response to repeated identical stimuli of 2–10 s duration that was dependent on the time interval between stimuli. Responses to paired current steps, bypassing the signal transduction cascade, also showed spike frequency adaptation. We found that voltage-gated Na+ channels in VSNs undergo slow inactivation processes. Furthermore, recovery from slow inactivation of voltage-gated Na+ channels occurs in several seconds, a time scale similar to that measured during paired-pulse adaptation protocols, suggesting that it partially contributes to short-term spike frequency adaptation. We conclude that vomeronasal neurons do exhibit a time-dependent short-term spike frequency adaptation to repeated natural stimuli and that slow inactivation of Na+ channels contributes to this form of adaptation. These findings not only increase our knowledge about adaptation in the vomeronasal system, but also raise the question of whether slow inactivation of Na+ channels may play a role in other sensory systems.

Long-Term Inactivation of Sodium Channels as a Mechanism of Adaptation in CA1 Pyramidal Neurons.

Many hippocampal CA1 pyramidal cells function as place cells, increasing their firing rate when a specific place field is traversed. The dependence of CA1 place cell firing on position within the place field is asymmetric. We investigated the source of this asymmetry by injecting triangular depolarizing current ramps to approximate the spatially tuned, temporally diffuse depolarizing synaptic input received by these neurons while traversing a place field. Ramps were applied to CA1 pyramidal neurons from male rats in vitro (slice electrophysiology) and in silico (multicompartmental NEURON model). Under control conditions, CA1 neurons fired more action potentials at higher frequencies on the up-ramp versus the down-ramp. This effect was more pronounced for dendritic compared with somatic ramps. We incorporated a four-state Markov scheme for NaV1.6 channels into our model and calibrated the spatial dependence of long-term inactivation according to the literature; this spatial dependence was sufficient to explain the difference in dendritic versus somatic ramps. Long-term inactivation reduced the firing frequency by decreasing open-state occupancy, and reduced spike amplitude during trains by decreasing occupancy in the closed state, which comprises the available pool. PKC activator phorbol-dibutyrate, known to reduce NaV long-term inactivation, removed spike amplitude attenuation in vitro more visibly in dendrites and greatly reduced adaptation, consistent with our hypothesized mechanism. Intracellular application of a peptide inducing long-term NaV inactivation elicited spike amplitude attenuation during spike trains in the soma and greatly enhanced adaptation. Our synergistic experimental/computational approach shows that long-term inactivation of NaV1.6 is a key mechanism of adaptation in CA1 pyramidal cells.SIGNIFICANCE STATEMENT The hippocampus plays an important role in certain types of memory, in part through context-specific firing of "place cells"; these cells were first identified in rodents as being particularly active when an animal is in a specific location in an environment, called the place field of that neuron. In this in vitro/in silico study, we found that long-term inactivation of sodium channels causes adaptation in the firing rate that could potentially skew the firing of CA1 hippocampal pyramidal neurons earlier within a place field. A computational model of the sodium channel revealed differential regulation of spike frequency and amplitude by long-term inactivation, which may be a general mechanism for spike frequency adaptation in the CNS.

Kisspeptin-13 prevented the electrophysiological alterations induced by amyloid-beta pathology in rat: Possible involvement of stromal interaction molecules and pCREB

Alzheimer's disease (AD) is a progressive neurological disease that slowly causing memory impairments with no effective treatment. We have recently reported that kisspeptin-13 (KP-13) ameliorates Aβ toxicity-induced memory deficit in rats. Here, the possible cellular impact of kisspeptin receptor activation in a rat model of the early stage AD was assessed using whole-cell patch-clamp recording from CA1 pyramidal neurons and molecular approaches. Compared to neurons from the control group, cells from the Aβ-treated group displayed spontaneous and evoked hyperexcitability with lower spike frequency adaptation. These cells had also a lower sag ratio in response to hyperpolarizing prepulse current delivered before a depolarizing current injection. Neurons from the Aβ-treated group exhibited short spike onset latency, lower rheobase and short utilization time compared with those in the control group. Furthermore, phase plot analysis of action potential showed that Aβ treatment affected the action potential features. These electrophysiological changes induced by Aβ were associated with increased expression of stromal interaction molecules (STIMs), particularly (STIM2) and decreased pCREB/CREB ratio. Treatment with KP-13 following Aβ injection into the entorhinal cortex, however, prevented the excitatory effect of Aβ on spontaneous and evoked neuronal activity, increased the latency of onset, enhanced the sag ratio, increased the rheobase and utilization time, and prevented the changes induced Aβ on spike parameters. In addition, the KP-13 application after Aβ treatment reduced the expression of STIMs and increased the pCREB/CREB ratio compared to those receiving Aβ treatment alone. In summary, these results provide evidence that activation of kisspeptin receptor may be effective against pathology of Aβ.

Functionally-distinct pyramidal cell subpopulations during gamma oscillations in mouse hippocampal area CA3.

Gamma oscillations (γ-oscillations) in hippocampal area CA3 are essential for memory function. Particularly, CA3 is involved in the memory related process pattern completion, which is linked with the γ-oscillations in human hippocampus. Recent studies suggest that heterogeneity in the functional properties of pyramidal cells (PCs) in CA3 plays an important role in hippocampal function. By performing concomitant recordings of PC activity and network γ-oscillations in CA3 we found three functionally-different PC subpopulations. PCs with high spike-frequency adaptation (hAPC) have the strongest action potential gamma phase-coupling, PCs with low adaptation (lAPC) show lower phase-coupling and PCs displaying a burst-firing pattern (BPC) remained quiescent. In addition, we discovered that hAPC display the highest excitatory/inhibitory drive, followed by lAPC, and lastly BPC. In conclusion, our data advance the hypothesis that PCs in CA3 are organized into subpopulations with distinct functional roles for cognition-relevant network dynamics and provide new insights in the physiology of hippocampus.

Spiking Recurrent Neural Networks Represent Task-Relevant Neural Sequences in Rule-Dependent Computation.

Prefrontal cortical neurons play essential roles in performing rule-dependent tasks and working memory-based decision making. Motivated by PFG recordings of task-performing mice, we developed an excitatory-inhibitory spiking recurrent neural network (SRNN) to perform a rule-dependent two-alternative forced choice (2AFC) task. We imposed several important biological constraints onto the SRNN, and adapted spike frequency adaptation (SFA) and SuperSpike gradient methods to train the SRNN efficiently. The trained SRNN produced emergent rule-specific tunings in single-unit representations, showing rule-dependent population dynamics that resembled experimentally observed data. Under varying test conditions, we manipulated the SRNN parameters or configuration in computer simulations, and we investigated the impacts of rule-coding error, delay duration, recurrent weight connectivity and sparsity, and excitation/inhibition (E/I) balance on both task performance and neural representations. Overall, our modeling study provides a computational framework to understand neuronal representations at a fine timescale during working memory and cognitive control, and provides new experimentally testable hypotheses in future experiments.

Correlation of Electrophysiological and Gene Transcriptional Dysfunctions in Single Cortical Parvalbumin Neurons After Noise Trauma

Parvalbumin-expressing (PV+) interneurons in the sensory cortex form powerful inhibitory synapses on the perisomatic compartments and axon initial segments of excitatory principal neurons (PNs), and perform diverse computational functions. Impaired PV+ interneuron functions have been reported in neural developmental and degenerative disorders. Expression of the unique marker parvalbumin (PV) is often used as a proxy of PV+ interneuron functions. However, it is not entirely clear how PV expression is correlated with PV+ interneuron properties such as spike firing and synaptic transmission. To address this question, we characterized electrophysiological properties of PV+ interneurons in the primary auditory cortex (AI) using whole-cell patch clamp recording, and analyzed the expression of several genes in samples collected from single neurons using the patch pipettes. We found that, after noise induced hearing loss (NIHL), the spike frequency adaptation increased, and the expression of PV, glutamate decarboxylase 67 (GAD67) and Shaw-like potassium channel (KV3.1) decreased in PV+ neurons. In samples prepared from the auditory cortical tissue, the mRNA levels of the target genes were all pairwise correlated. At the single neuron level, however, the expression of PV was significantly correlated with the expression of GAD67, but not KV3.1, maximal spike frequency, or spike frequency adaptation. The expression of KV3.1 was correlated with spike frequency adaptation, but not with the expression of GAD67. These results suggest separate transcriptional regulations of PV/GAD67 vs. KV3.1, both of which are modulated by NIHL.

A neuromorphic model of olfactory processing and sparse coding in the Drosophila larva brain

Abstract Animal nervous systems are highly efficient in processing sensory input. The neuromorphic computing paradigm aims at the hardware implementation of neural network computations to support novel solutions for building brain-inspired computing systems. Here, we take inspiration from sensory processing in the nervous system of the fruit fly larva. With its strongly limited computational resources of <200 neurons and <1.000 synapses the larval olfactory pathway employs fundamental computations to transform broadly tuned receptor input at the periphery into an energy efficient sparse code in the central brain. We show how this approach allows us to achieve sparse coding and increased separability of stimulus patterns in a spiking neural network, validated with both software simulation and hardware emulation on mixed-signal real-time neuromorphic hardware. We verify that feedback inhibition is the central motif to support sparseness in the spatial domain, across the neuron population, while the combination of spike frequency adaptation and feedback inhibition determines sparseness in the temporal domain. Our experiments demonstrate that such small, biologically realistic neural networks, efficiently implemented on neuromorphic hardware, can achieve parallel processing and efficient encoding of sensory input at full temporal resolution.

Combining Accuracy and Plasticity in Convolutional Neural Networks Based on Resistive Memory Arrays for Autonomous Learning

Nowadays, artificial neural networks (ANNs) can outperform the human brain’s ability in specific tasks. However, ANNs cannot replicate the efficient and low-power learning, adaptation, and consolidation typical of biological organisms. Here, we present a hardware design based on arrays of SiO x resistive switching random-access memory (RRAM), which allows combining the accuracy of convolutional neural networks with the flexibility of bio-inspired neuronal plasticity. In order to enable the combination of the stable and the plastic attributes of the network, we exploit the spike-frequency adaptation of the neurons relying on the multilevel programming of the RRAM devices. This procedure enhances the efficiency and accuracy of the network for MNIST, noisy MNIST (N-MNIST), Fashion-MNIST, and CIFAR-10 datasets, with inference accuracies of about 99%–89%, respectively. We also demonstrate that the hardware is capable of asynchronous self-adaptation of its operative frequency according to the fire rate of the spiking neuron, thus optimizing the whole behavior of the network. We finally show that the system enables fast and accurate filter retraining to overcome catastrophic forgetting, showing high efficiency in terms of operations per second and robustness against device non-idealities. This work paves the way for the theoretical modeling and hardware realization of resilient autonomous systems in dynamic environments.

Patterned stimulation leads in–vitro network into non-bursting state

Abstract Spontaneous synchronized neuronal activity is found in the neocortical areas of the developing as well as epileptic brain. Synchronized neuronal activity can also be found in dissociated neural networks in vitro. To study the mechanistic origin of this behavior, we built a simple and fast computational model of large network of approximately 80% excitatory and 20% inhibitory neurons. Our model precisely matched the spike times and spike frequency adaptation of cortical pyramidal neurons obtained via whole cell recordings in confined in vitro networks. High accuracy of our model was achieved by implementing an AHP current with sigmoid dynamics that approximated Ca2+ dependent spike frequency adaptation. Then, we implemented short term synaptic depression of the synaptic weights. Modeling of depression was verified by matching the experimental post synaptic responses with three different frequencies of spikes evocation in the presynaptic neuron using dual whole cell recording. Investigations with this computational network were then verified in an all-optical experimental system. This experimental setup was designed by confining a cortical network with polydimethylsiloxane (PDMS), and by co-transfecting the neurons with channel-rhodopsin 2 (ChR2) and jRgeco1a. Patterned stimulator with spatial resolution of somatic compartment (approximately 15 μm) was used to excite individual neuron in a 2D confined network consisting of approximately 300 neurons confined to an area of about 1 mm2. We explored different stimulation paradigms in silico and in vitro to achieve a burst free neuronal network of different size by activating different numbers and patterns of input neurons. Action potential adaptation of each spiking excitatory neuron and the short term synaptic depression of each activated synapses could lead the network into a non-bursting mode. Distributed stimulation method that was developed in this work could potentially be used in suppressing epileptic seizures. Keywords: burst free network, patterned stimulation, optogenetics, synaptic depression

Efficient and Accurate Computational Model of Neuron with Spike Frequency Adaptation.

Simplified models of neurons are widely used in computational investigations of large networks. One of the most important performance metrics of simplified models is their accuracy in reproducing action potential (spike) timing. In this article, we developed a simple, computationally efficient neuron model by modifying the adaptive exponential integrate and fire (AdEx) model [1] with sigmoid afterhyperpolarization current (Sigmoid AHP). Our model can precisely match the spike times and spike frequency adaptation of cortical pyramidal neurons. The accuracy was similar to a more complex two compartment biophysically realistic model of the same neurons. This work provides a simplified neuronal model with improved spike timing accuracy for use in modeling of large neural networks.Clinical Relevance- Accurate and computationally efficient single neuron model will enable large network modeling of brain regions involved in neurological and psychiatric disorders and may lead to a better understanding of the disorder mechanisms.

On numerical approximations of fractional-order spiking neuron models

Fractional-order spiking neuron models can enrich model flexibility and dynamics due to the extra degrees of freedom. This paper aims to study the effects of applying four different numerical methods to two fractional-order spiking neuron models: the Fractional-order Leaky integrate-and-fire (FO-LIF) model and the Fractional-order Hodgkin–Huxley (FO-HH) model. Furthermore, some adjustments to these models are proposed, and the effect of reducing the memory size is investigated. We first propose a new realistic formulation for Fo-LIF model to better describe its functionality that is aligned with the integer model. In addition, A finite memory window version of the L1 approximation is provided for fair comparison against the well-known techniques such as Grunwald–Letnikov (GL)-based method, the Rectangular product integration rule (PI-Rect), and Z-transform approaches. Spiking patterns, inter-spike interval (ISI) adaptation, and steady-state spiking frequency are studied for each numerical method under varying memory lengths. We observe that the adaptation direction differs between the numerical methods; for example the L1 approximation shows upward spike frequency adaptation, while the PI-Rect rule shows downward adaptation. It is found that the PI-Rect rule and similar methods are more capable of simulating the effect of the memory of the input signal than L1 approximation and similar methods. Furthermore, the L1 approximation cannot have a reliable finite memory version, as it can spike under zero input current, which makes it non-realistic. Finally, We provide time complexity comparison between the models showing that PI-Rect is 2x faster in simulation than L1 approximation, which is commonly used in fractional spiking neurons.

A User's Guide to Generalized Integrate-and-Fire Models.

The generalized integrate-and-fire (GIF) neuron model accounts for some of the most fundamental behaviours of neurons within a compact and extensible mathematical framework. Here, we introduce the main concepts behind the design of the GIF model in terms that will be familiar to electrophysiologists, and show why its simple design makes this model particularly well suited to mimicking behaviours observed in experimental data. Along the way, we will build an intuition for how specific neuronal behaviours, such as spike-frequency adaptation, or electrical properties, such as ionic currents, can be formulated mathematically and used to extend integrate-and-fire models to overcome their limitations. This chapter will provide readers with no previous exposure to modelling a clear understanding of the strengths and limitations of GIF models, along with the mathematical intuitions required to digest more detailed and technical treatments of this topic.

An area and energy efficient LIF neuron model with spike frequency adaptation mechanism

As neuron is the fundamental unit of the nervous system, it is one of the main building blocks in the spiking neural networks hardware implementation. To implement hardware that consists of many thousands of neurons and accurately mimics the brain functions, an energy and area-efficient design for the neuron is essential. In this paper, we propose a VLSI circuit for the leaky integrate and fire (LIF) neuron model, in 130 nm CMOS technology. The proposed neuron has some important features: first, it consumes very low energy; second, it is simple and area-efficient. Third, it has the spike frequency adaptation capability that makes it more biologically plausible. The spike frequency adaptation mechanism is added to the model only by one additional transistor, and it is done just by one parameter. The energy per spike of the neuron circuit in the worst case is only 0.67 fJ/spike. The spike frequency is in the MHz range, which enables attractive hardware acceleration.

A robust model of Stimulus-Specific Adaptation validated on neuromorphic hardware

Stimulus-Specific Adaptation (SSA) to repetitive stimulation is a phenomenon that has been observed across many different species and in several brain sensory areas. It has been proposed as a computational mechanism, responsible for separating behaviorally relevant information from the continuous stream of sensory information. Although SSA can be induced and measured reliably in a wide variety of conditions, the network details and intracellular mechanisms giving rise to SSA still remain unclear. Recent computational studies proposed that SSA could be associated with a fast and synchronous neuronal firing phenomenon called Population Spikes (PS). Here, we test this hypothesis using a mean-field rate model and corroborate it using a neuromorphic hardware. As the neuromorphic circuits used in this study operate in real-time with biologically realistic time constants, they can reproduce the same dynamics observed in biological systems, together with the exploration of different connectivity schemes, with complete control of the system parameter settings. Besides, the hardware permits the iteration of multiple experiments over many trials, for extended amounts of time and without losing the networks and individual neural processes being studied. Following this “neuromorphic engineering” approach, we therefore study the PS hypothesis in a biophysically inspired recurrent networks of spiking neurons and evaluate the role of different linear and non-linear dynamic computational primitives such as spike-frequency adaptation or short-term depression (STD). We compare both the theoretical mean-field model of SSA and PS to previously obtained experimental results in the area of novelty detection and observe its behavior on its neuromorphic physical equivalent model. We show how the approach proposed can be extended to other computational neuroscience modelling efforts for understanding high-level phenomena in mechanistic models.

Interspike interval correlations in neuron models with adaptation and correlated noise.

The generation of neural action potentials (spikes) is random but nevertheless may result in a rich statistical structure of the spike sequence. In particular, contrary to the popular renewal assumption of theoreticians, the intervals between adjacent spikes are often correlated. Experimentally, different patterns of interspike-interval correlations have been observed and computational studies have identified spike-frequency adaptation and correlated noise as the two main mechanisms that can lead to such correlations. Analytical studies have focused on the single cases of either correlated (colored) noise or adaptation currents in combination with uncorrelated (white) noise. For low-pass filtered noise or adaptation, the serial correlation coefficient can be approximated as a single geometric sequence of the lag between the intervals, providing an explanation for some of the experimentally observed patterns. Here we address the problem of interval correlations for a widely used class of models, multidimensional integrate-and-fire neurons subject to a combination of colored and white noise sources and a spike-triggered adaptation current. Assuming weak noise, we derive a simple formula for the serial correlation coefficient, a sum of two geometric sequences, which accounts for a large class of correlation patterns. The theory is confirmed by means of numerical simulations in a number of special cases including the leaky, quadratic, and generalized integrate-and-fire models with colored noise and spike-frequency adaptation. Furthermore we study the case in which the adaptation current and the colored noise share the same time scale, corresponding to a slow stochastic population of adaptation channels; we demonstrate that our theory can account for a nonmonotonic dependence of the correlation coefficient on the channel's time scale. Another application of the theory is a neuron driven by network-noise-like fluctuations (green noise). We also discuss the range of validity of our weak-noise theory and show that by changing the relative strength of white and colored noise sources, we can change the sign of the correlation coefficient. Finally, we apply our theory to a conductance-based model which demonstrates its broad applicability.

A Brain-Inspired Homeostatic Neuron Based on Phase-Change Memories for Efficient Neuromorphic Computing.

One of the main goals of neuromorphic computing is the implementation and design of systems capable of dynamic evolution with respect to their own experience. In biology, synaptic scaling is the homeostatic mechanism which controls the frequency of neural spikes within stable boundaries for improved learning activity. To introduce such control mechanism in a hardware spiking neural network (SNN), we present here a novel artificial neuron based on phase change memory (PCM) devices capable of internal regulation via homeostatic and plastic phenomena. We experimentally show that this mechanism increases the robustness of the system thus optimizing the multi-pattern learning under spike-timing-dependent plasticity (STDP). It also improves the continual learning capability of hybrid supervised-unsupervised convolutional neural networks (CNNs), in terms of both resilience and accuracy. Furthermore, the use of neurons capable of self-regulating their fire responsivity as a function of the PCM internal state enables the design of dynamic networks. In this scenario, we propose to use the PCM-based neurons to design bio-inspired recurrent networks for autonomous decision making in navigation tasks. The agent relies on neuronal spike-frequency adaptation (SFA) to explore the environment via penalties and rewards. Finally, we show that the conductance drift of the PCM devices, contrarily to the applications in neural network accelerators, can improve the overall energy efficiency of neuromorphic computing by implementing bio-plausible active forgetting.

Spike frequency adaptation supports network computations on temporally dispersed information.

For solving tasks such as recognizing a song, answering a question, or inverting a sequence of symbols, cortical microcircuits need to integrate and manipulate information that was dispersed over time during the preceding seconds. Creating biologically realistic models for the underlying computations, especially with spiking neurons and for behaviorally relevant integration time spans, is notoriously difficult. We examine the role of spike frequency adaptation in such computations and find that it has a surprisingly large impact. The inclusion of this well-known property of a substantial fraction of neurons in the neocortex - especially in higher areas of the human neocortex - moves the performance of spiking neural network models for computations on network inputs that are temporally dispersed from a fairly low level up to the performance level of the human brain.

A novel density-based neural mass model for simulating neuronal network dynamics with conductance-based synapses and membrane current adaptation

Despite its success in understanding brain rhythms, the neural mass model, as a low-dimensional mean-field network model, is phenomenological in nature, so that it cannot replicate some of rich repertoire of responses seen in real neuronal tissues. Here, using a colored-synapse population density method, we derived a novel neural mass model, termed density-based neural mass model (dNMM), as the mean-field description of network dynamics of adaptive exponential integrate-and-fire (aEIF) neurons, in which two critical neuronal features, i.e., voltage-dependent conductance-based synaptic interactions and adaptation of firing rate responses, were included. Our results showed that the dNMM was capable of correctly estimating firing rate responses of a neuronal population of aEIF neurons receiving stationary or time-varying excitatory and inhibitory inputs. Finally, it was also able to quantitatively describe the effect of spike-frequency adaptation in the generation of asynchronous irregular activity of excitatory–inhibitory cortical networks. We conclude that in terms of its biological reality and calculation efficiency, the dNMM is a suitable candidate to build significantly large-scale network models involving multiple brain areas, where the neuronal population is the smallest dynamic unit.