Abstract
Working memory (WM) can maintain sequential and concurrent information, and the load enhances the γ band oscillation during the delay period. To provide a unified account for these phenomena in working memory, we investigated a continuous network model consisting of pyramidal cells, high-threshold fast-spiking interneurons (FS), and low-threshold nonfast-spiking interneurons (nFS) for working memory of sequential and concurrent directional cues. Our model exhibits the γ (30–100 Hz) and β (10–30 Hz) band oscillation during the retention of both concurrent cues and sequential cues. We found that the β oscillation results from the interaction between pyramidal cells and nFS, whereas the γ oscillation emerges from the interaction between pyramidal cells and FS because of the strong excitation elicited by cue presentation, shedding light on the mechanism underlying the enhancement of γ power in many cognitive executions.
- oscillation
- sequential/concurrent items
- spiking network
- working memory
Significance Statement
We constructed a spiking network to perform working memory (WM) tasks with sequentially or concurrently presented items. The model exhibits the coexistence of β (10–30 Hz) and γ (30–100 Hz) band oscillations during the delay period. We found that γ and β band oscillations recruit separate neural circuits. The low-threshold nonfast-spiking (nFS) neurons are involved in the β band oscillation, whereas the high-threshold fast-spiking (FS) neurons are involved in the γ band oscillation. Our results shed light on the well-known phenomenon that cognitive tasks enhance γ band oscillations.
Introduction
Working memory (WM), the ability to actively maintain and manipulate information in the absence of stimuli, plays a crucial role in cognitive function and executive control of behavior (Baddeley, 2003). The information maintained in WM can enter the brain concurrently or sequentially, meaning that WM cannot only hold multiple pieces of information arriving simultaneously as in visuospatial WM tasks (Bays and Husain, 2008; Zhang and Luck, 2008; Gorgoraptis et al., 2011), but also information presented sequentially, as in visual (Gorgoraptis et al., 2011) or speech processing (Cowan, 2010). The neural implementation of information maintenance is not well understood. The oscillatory model proposes that the information of one item is represented in WM by the reactivation of neurons in the γ cycle within nested γ-theta oscillations, mediated by a slow after-depolarization (ADP) with a time constant that should match the theta oscillation (Lisman and Idiart, 1995; Jensen and Lisman, 1998). The dynamic model posits that memory is maintained by item-specific patterns of synaptic plasticity and that neurons exhibit a nonstationary and short-lived attractor activity, in which only one memory representation can be active at a time, but successive reactivations of neuronal pools memorize different items (Mongillo et al., 2008; Lundqvist et al., 2016, 2018; Mi et al., 2017). The persistent activity model asserts that persistent activity of neurons induced by N-methyl-D-aspartate receptor (NMDAR)-mediated recurrent synaptic current encodes the corresponding items during the delay period (Compte et al., 2000; Edin et al., 2009; Wei et al., 2012). The ability of WM to maintain concurrent and sequential information challenges these models. On the one hand, the oscillatory model and dynamic models can use oscillatory activity to maintain sequential information, but it is difficult for them to represent concurrent information (Constantinidis et al., 2018); on the other hand, the persistent models can manipulate concurrent information but cannot address sequential stimuli and often focus on the persistent activity without oscillation. Experiments have shown that γ band oscillations are involved in WM maintenance, in particular, that WM load enhances the γ band oscillations but suppresses the alpha band oscillations. Here, we aim to construct a biophysically plausible network model to implement the storage of concurrent and sequential information and to investigate how WM load alters oscillations in the network.
The biological brain consists of microcircuits with three types of neurons that play key roles in generating oscillations: pyramidal cells, low-threshold nonfast-spiking (nFS), and high-threshold neuronal FS (Chen et al., 2017; Veit et al., 2017; Domhof and Tiesinga, 2021; Hahn et al., 2022). The pyramidal cells are excitatory, while the FS and nFS are GABAergic inhibitory. Pyramidal cells synapse on nFS and FS. In turn, nFS neurons synapse back onto the dendrites of the pyramidal cell, while FS neurons synapse back onto the soma of the pyramidal cell, creating a dynamic feedback loop that regulates excitatory and inhibitory activities. FS and nFS also inhibit each other, resulting in a competitive relationship. At rest, the nFS is more active, inhibiting the FS and forming a β oscillation with the pyramidal cells. When a perceptual stimulus is presented, the FS cells are activated, inhibiting the nFS cells and creating a γ oscillation with the pyramidal cells (Chen et al., 2017; Hahn et al., 2022). Given the importance of oscillations in higher cognitive functions, we aim to explore the role of oscillations and microcircuitry in working memory.
We proposed a spiking neural network to implement WM with concurrent and sequential directional information (Gorgoraptis et al., 2011). The network consists of two-compartment pyramidal cells, nFS, and FS. These cells and compartments are interconnected as in the biological brain. The cells are uniformly arranged in a ring according to their preferred direction (Compte et al., 2000). When activated, pyramidal cells activate pyramidal cells with similar preferences as well as nearby FS cells and nFS cells on the ring. FS and nFS inhibit neighboring neurons on the ring. We found that, regardless of whether the directions are presented concurrently or sequentially, the network elicits corresponding localized activities that persist throughout the delay period. The persistent activity suggests that the model can successfully maintain multiple stimuli presented simultaneously or sequentially in working memory. Furthermore, before cue presentation, the interaction between pyramidal cells and nFS dominates the activity of the network and causes a low-band oscillation (10–30 Hz); the cue presentation induces strong excitation and recruits FS into the network, and the interaction between pyramidal cells and FS dominates the activity of the network, enhancing the γ oscillation (35–100 Hz), which persists throughout the delay period.
Materials and Methods
Model architecture
Our model has 4096 excitatory pyramidal cells, 512 FS cells, and 512 nFS cells. We have two reasons to choose the number of neurons. First, the ratio of the number of excitatory neurons over that of inhibitory neurons is ∼4:1 (Braitenberg and Schutz, 1991). Thus, we chose 4906 pyramidal neurons and 1024 inhibitory interneurons (FS + nFS, 1024). Second, a network size of
- From pyramidal cells soma to pyramidal cells soma (
- From pyramidal cells soma to FS cell (
- From pyramidal cells soma to nFS cell (
- From FS cell to pyramidal cells soma (
- From FS cell to nFS cell (
- From nFS cell to pyramidal cells dendrite (
The value of parameters
Neuron models
Pyramidal cells and inhibitory cells follow the leaky integrate-and-fire model (Tuckwell, 1988). The neurons generate spikes when their membrane potentials reach threshold (
Synapse models
Synaptic currents are mediated by AMPA, NMDA, and GABA transmissions. Three types of synaptic currents follow the equations:
The conductance “g” in our calculations primarily follows the Equation 2,
Synapses from Pyrs to nFS mediated by NMDAR were facilitated in short term and follow the equation:
U is set to 0.4 in our simulation (Mongillo et al., 2008).
Simulation protocol
We simulated two types of WM tasks: concurrent-cue tasks (CCTs) and sequential-cue tasks (SCTs), similar to the experiments in (Gorgoraptis et al., 2011). In CCTs, cues are presented to neurons concurrently; each pyramidal cell indexed by
θ received a current
In SCTs, pyramidal cells received multiple currents sequentially. Each current can be formulated as:
The kth current is applied at
Noise implementation
Noise is applied to pyramidal cells and inhibitory cells via AMPA synapses:
Starting parameters
To establish a more realistic and stable initial condition, we conducted a recording procedure to obtain a consistent value of multiple variables for each type of neurons. The following variables were used: the membrane potential (“
Numeric integration and code accessibility
The model was programmed in MATLAB code and the gating variables were integrated using a second-order Runge–Kutta (RK2) algorithm.
The membrane potential is integrated with Euler scheme as Equations 3–6. This different choice in the integration method is because changes in gating variables during neuronal firing are more complex and usually nonlinear, whereas changes in the subthreshold membrane potential are approximately linear. We conducted a convergence test on two neurons with AMPA connections and provided a 2-mV constant stimulus to the presynaptic neuron for 2000 ms to examine the difference of spike time of postsynaptic neuron between Euler-RK2 and RK2 methods. We found that the spike time errors generated by the two methods are almost negligible for sufficiently small dt (such as 0.02, as in the experiments). By variation of the timesteps, we found the convergence order of integration method
This approach reduces the computational cost without compromising accuracy. The code/software described in the paper is freely available online at https://github.com/scientific-lab/Gamma-and-Beta-Band-Oscillation-in-Working-Memory. We performed our research using GPU(K80) with Ubuntu 18.04.6 LTS, Microsoft Surface Pro 5 with Windows 10 operating system, and MacBook Pro 11,1 with Windows 10 operating system.
Data analysis
Firing rate calculation
We recorded the firing activities of neurons (see Fig. 2) and counted the spikes of a certain type of neuron along the time in a single trial and approximated the firing rate by applying a one-dimension filter with a window size of 4 ms to the spike train.
Local field potential and spectrograms
We approximated the local field potential (LFP) using the total synaptic currents onto pyramidal cells following the method proposed by Mazzoni et al. (2015):
Burst rate calculation
A burst in a particular frequency is defined as an interval when spectral power exceeds twice the standard deviation above the average value of that frequency and lasts for at least three cycles (Lundqvist et al., 2016). After we located the bursts in a single trail, we applied the same process to multiple trails and calculated the frequency of the bursts in the β (10–30 Hz) or γ (35–100 Hz) band over time. Similar to how we extracted the firing rate, we filtered the calculated burst frequency with a one-dimensional Gaussian filter, with a window size of 60 ms, to approximate the burst rate for a certain frequency range.
Statistical comparison
In our study, we employed independent sample t tests and ANOVA to compare between groups after conducting tests for homogeneity of variance and making necessary corrections. We also provided the effect size and confidence interval for each significant test result. When necessary, we conducted multiple comparison analyses to offer comprehensive results. All statistical analyses were performed using MATLAB. Our model generated substantial data from multiple simulations with high power and narrow confidence intervals. Based on the results from the online power and sample size calculator (https://www.gigacalculator.com/calculators/), the power for all statistical tests were close to 100%.
Results
The continuous spiking network can hold concurrent and sequential cues
We considered a spiking neural network consisting of one population of 4098 pyramidal neurons, one population of 512 FS, and one population of 512 nFS. Pyramidal cells are uniformly distributed on a ring according to their preferred cue angles (Fig. 1A; Compte et al., 2000), mimicking the columnar organization of the monkey PFC (Goldman-Rakic, 1995; Rao et al., 1999; Constantinidis et al., 2001). Using this continuous spiking network model, we investigated how WM manipulates sequential and concurrent cues. We first presented one direction to the network for a brief period and then withdraw the cue to explore the spatiotemporal pattern of neural activity elicited by the cue presentation. As shown in Figure 2A, pyramidal cells showed relatively sparse and regular discharges in the absence of stimulus from 0 to 500 ms. At 500–700 ms, the stimulation elicited an intense firing in the cued direction. When the cue was withdrawn, this intense discharge in the preferred direction persisted and showed oscillatory activity throughout the 700- to 2000-ms delay period. We call this sustained oscillatory activity in the cued direction “oscillatory activity bump” and believe it maintains information about the cue. Pyramidal cells in other directions were unaffected during and after the cue presentation and continued to discharge sparsely and regularly. The inhibitory interneurons nFS (Fig. 2B, blue) and FS (Fig. 2B, red) showed opposite trends. In the prestimulus period (0–500 ms), the nFS discharged as sparsely and regularly as the pyramidal cell, whereas the FS barely discharged. Within 500–700 ms of stimulus presentation, the nFS stopped firing in the preferred direction, whereas the FS began to fire strongly and intensely in the preferred direction. This firing pattern continued throughout the 700- to 2000-ms delay period after stimulus withdrawal. The stimulus had little effect on the nonpreferred nFS and FS, and their spikes remained sparse or absent. Figure 4B provides additional information about the average membrane potential of the cells. It also shows examples of pyramidal cells, FS, and nFS in the preferred and nonpreferred directions. We then presented two or three directional cues to the network sequentially or concurrently. Given concurrent cues, the network simultaneously elicits two or three distinct oscillatory activity bumps that persist throughout the delay period (Fig. 2C,D), and each distinct activity bump maintains the information of one cued direction. For sequential cues, the network elicits distinct oscillatory activity bumps one after another (Fig. 2E,F). The later presented cue evokes a new persistent oscillatory activity bump and does not disrupt the persistent oscillatory activity bump elicited by the previous cue. These results show that our continuous network can store not only concurrent information but also sequential information of WM.
Labor division of interneurons in γ and β oscillation
To investigate the oscillatory behavior in WM, we calculated the firing rate of pyramidal cells and the LFP given one directional cue in a single trial (Fig. 3). The population firing rate and LFP oscillate before and after the cue presentation and throughout the delay period (Fig. 3C,E). The spectrogram of the LFP shows that the network oscillates in the β band before the cue presentation but oscillates in the β and γ bands during the delay period (Fig. 3D,F). Figures 4 and 5 provide an overview of the average membrane potential (Fig. 4A) and gating variables (Fig. 5A) during a single trial for different neurons or compartments. In addition, these figures provide examples of individual neurons in both preferred and nonpreferred directions (Figs. 4B, 5B). Overall, the mean membrane potential of pyramidal cells [Mpre = −55.054, Maft = −54.918, t = −43.399, p < 0.001, d = 0.430, cl = [−0.142,−0.130] (Fig. 4A), where Mpre is the average membrane potential before cue presentation and Maft represets that after the cue] and FS (Mpre = −67.212, Maft = −67.117, t = −24.981, p < 0.001, d = 0.224, cl = [−0.103,−0.088]; Fig. 4A) increased, and that of nFS cells decreased after stimulus presentation (Mpre = −53.999, Maft = −54.598, t = 542.997, p < 0.001, d = 3.464, cl = [0.596,0.601]; Fig. 4A). We calculated the total synaptic currents received by the interneurons (Fig. 6). We found that the cue presentation increases the synaptic input to FS close to the presented direction (Mpre = 0.057, Maft = 0.330, t = −271.328, p < 0.001, d = 1.360, cl = [−0.274,−0.270]; Fig. 6A) and activates these FS (Fig. 2B) during the cue presentation and throughout the delay period. Cue presentation has little effect on the synaptic input to FS far away from the cued direction (Mpre = 0.049, Maft = 0.041, t = 13.920, p < 0.001, d = 0.092, cl = [0.007,0.009]; Fig. 6B) and cannot activate them (Fig. 2B). We found that synaptic currents input to nFS near the cued direction decrease because of the inhibition from active FS during cue presentation and the delay period (Mpre = 0.081, Maft = −0.215, t = 188.834, p < 0.001, d = 0.820, cl = [0.293,0.299]; Fig. 6C), and these nFS are suppressed (Fig. 2B). However, the input to nFS far away from the cued direction does not change much (Mpre = 0.079, Maft = 0.080, t = −3.175, p = 0.002, d = 0.021, cl = [−0.002,−0.000]; Fig. 6D). In summary, before the cue presentation, weak excitation is insufficient to activate FS, and the interaction between pyramidal cells and nFS dominates the network activity, resulting in β band oscillation. The cue presentation induces strong excitation and activates FS, and the interaction between pyramidal cells and FS leads to γ band oscillation in the network. Therefore, these results show a division of labor between FS and nFS in γ band and the β band oscillations, as shown in Figure 6E.
γ Power increases in the cued direction
Experiments (Pesaran et al., 2002) showed that the increase in γ band power was sustained during the delay period in the preferred direction, while γ band power did not change in the anti-preferred direction. We calculated the LFP based on the pyramidal cells close to the cued direction, which is the preferred direction, and the pyramidal cells far away from the cued direction, which is the anti-preferred direction. We found that in the preferred direction, the LFP mainly consists of β band oscillation before the cue presentation, and a strong γ band oscillation appears after the cue presentation in a single trial (Fig. 7A) or averaged over trials (Fig. 7C). The power of γ band increases significantly (Mpre = 23.942, Maft = 26.616, t = −80.835, p < 0.001, d = 0.784, cl = [−2.739,−2.609]), but the power of β band decreases (Mpre = 37.157, Maft = 35.381, t = 76.696, p < 0.001, d = 0.737, cl = [1.731,1.822]; Fig. 7E). There is little increase in γ band oscillation (Mpre = 26.370, Maft = 26.741, t = −54.777, p < 0.001, d = 0.479, cl = [−0.385,−0.358]; Fig. 7B) for the anti-preferred direction, and the β band oscillation is not decreased (Mpre = 37.752, Maft = 37.450, t = 47.799, p < 0.001, d = 0.348, cl = [0.289,0.314]; Fig. 7F). Note that there was an early increase in γ and β band power in Figure 7E,F. We think this was an artifact of the cwtft algorithm. CWTFT calculates the power of a signal by locally integrating the multiplication of signals with shiftable symmetric wavelets of different window sizes and center frequencies. In the same wavelet window, wavelet analysis is isotropic, thus unable to distinguish components before and after stimulus presentation. When the signal frequency undergoes significant changes within a certain window, the window that contains the changing signal cannot differentiate between the prechange and postchange components, showing an earlier increase in power (similar to Kaiju et al., 2017). In short, cue presentation increases the power of the γ oscillation and decreases β oscillation in the preferred direction (Fig. 7E,F), which are consistent with the experimental observations (Pesaran et al., 2002).
WM load enhances the γ power during the delay period
We further analyzed the spectrogram of LFP given concurrent or sequential cues. The average spectrogram of LFP over 500 trials exhibits several characteristics. First, as previously shown, the network exhibits β band oscillation before the cue presentation, and the β power tend to decreases after cue presentation (two concurrent cues: Mpre = 107.469, Maft = 102.083, t = 39.865, p < 0.001, d = 0.412, cl = [5.121,5.651]; three concurrent cues: Mpre = 117.394, Maft = 108.605, t = 25.929, p < 0.001, d = 0.272, cl = [8.124,9.453]; two sequential cues: Mpre = 100.995, Maft = 97.639, t = 83.571, p < 0.001, d = 0.549, cl = [3.277,3.435]; three sequential cues: Mpre = 98.238, Maft = 98.990, t = −15.451, p < 0.001, d = 0.085, cl = [−0.847,−0.657]). Second, the network elicits higher γ band oscillation (around 60–70 Hz) on cue presentation (two concurrent cues: Mpre = 62.763, Maft = 68.745, t = −43.986, p < 0.001, d = 0.450, cl = [−6.249,−5.715]; three concurrent cues: Mpre = 68.370, Maft = 75.840, t = −21.950, p < 0.001, d = 0.233, cl = [−8.137,−6.803]; two sequential cues: Mpre = 58.800, Maft = 66.297, t = −133.050, p < 0.001, d = 1.081, cl = [−7.607,−7.386]; three sequential cues: Mpre = 58.202, Maft = 70.287, t = −193.409, p < 0.001, d = 1.299, cl = [−12.207,−11.963]) and γ band oscillation maintains during the delay period. Third, the γ power remains almost constant during the delay period for concurrent cues (Fig. 8A,B). In contrast, the γ power increases along with the arrival of new cues in sequential situations and the power is maintained throughout the delay period (two sequential cues: F = 13,057.29, p < 0.001;
We performed a Fourier analysis on the LFP given concurrent cues, and the results are consistent with the spectrogram analysis. γ power increases with the WM load (F = 71.06, p < 0.001; Fig. 9). Three cues lead to the highest γ power and one cue results in the lowest γ power. The mean of the γ power are M1cue = 0.493, M2cue = 1.060, and M3cue = 2.357 for 1, 2, and 3 cues, respectively (multiple comparisons p12 = 0.002, p13 < 0.001, p23 < 0.001; Fig. 9). The dependence of γ power on WM load can be explained by network activity. Strong excitation induced by cue presentation activates high-threshold FS and the interaction between pyramidal cells and FS results in γ oscillation. One cue elicits one distinct activity bump with γ oscillation and more cues elicit more distinct activity bumps with γ oscillation (Fig. 2), suggesting that more neurons participate in γ oscillation and result in higher γ power.
Brief bursts of narrow-band γ and β oscillation in a single trial
Although the raster plots of spiking in Figure 2 show oscillatory activity, the single spectrogram of LFP (Figs. 3B, 7A,B) shows a different scenario: γ and β oscillations occurred irregularly in the form of short bursts that are narrow and variable in frequency. This observation is similar to a previous monkey experiment (Lundqvist et al., 2016). Here, we identified the γ and β bursts using the algorithm proposed in the reference (Lundqvist et al., 2016). We identified each burst in 500 trials with sequential cues and plotted each burst as a point in Figure 10A. We also calculated the β and γ burst rates (Fig. 10B). The β burst rate decreases slightly after cue presentation (Mpre = 0.0207, Maft = 0.0196, t = 21.08, p < 0.001; Fig. 7F) and remains almost constant during the delay period (Fig. 10B). The γ burst rate increases when the cue is presented (Mpre = 0.0570, Maft = 0.0685, t = −48.93, p < 0.001; Fig. 7F) and increases with increasing WM load (F = 229.69, p < 0.001, multiple comparisons p < 0.001; Fig. 10B). During the delay period, the γ burst shows a slightly increasing trend, which is consistent with the experimental observation by Lundqvist et al. (2016; their Figure 8B).
Discussion
We have developed a working memory model that utilizes a continuous attractor structure based on the bistable excitatory-inhibitory microcircuit consisting of FS, nFS, and pyramidal cells in PFC. The main contributions of our work are threefold. First, the proposed model can maintain the information of sequentially presented cues and concurrently presented cues. Previous oscillatory and dynamic models collapse in the presence of concurrent information because these models rely on the oscillatory phase or timing to encode the information (Constantinidis et al., 2018). The persistent activity model has not been used to manipulate sequential information (Edin et al., 2009; Wei et al., 2012). Second, we identified the mechanism underlying the enhancement of γ power in the WM task. Cue presentation elicits strong excitatory recurrent synaptic currents that activate high-threshold FS. The interaction between pyramidal cells and FS leads to γ oscillations and enhances γ power during the delay period. Third, we demonstrated the division of labor between high-threshold FS and nFS for oscillation in the WM. The interaction between nFS and pyramidal cells leads to β oscillation, while the interaction between FS and pyramidal cells leads to γ oscillation.
We showed that the transient and sparkling γ and β burst in a single spectrogram of LFP can be approximated from the persistent oscillatory activity of the network (Fig. 3B), and the γ burst rate increases with the WM load (Fig. 8B). The discrete and sparkling γ and β burst in a single LFP spectrogram has been considered as evidence that WM is manifested through transient or discrete oscillatory dynamics, rather than sustained firing (Lundqvist et al., 2016). However, our results indicate that transient and sparkling γ and β bursts in a single LFP spectrogram may indeed result from persistent oscillatory firing. Therefore, our results favor the theory of persistent activity underlying WM.
In this work, we have demonstrated the division of labor between FS and nFS in generating γ and β oscillation. The interplay between high-threshold FS and pyramidal cells results in the γ oscillation, whereas the interaction between nFS and pyramidal cells leads to the β oscillation. Before the cue presentation in WM tasks, the weak recurrent excitation from spontaneous discharges of pyramidal cells can activate low-threshold nFS but not the high-threshold FS, resulting in β oscillation. Cue presentation evokes strong activity of pyramidal cells and strong recurrent excitation to activate FS. As a result, the network exhibits γ oscillation. This observation is consistent with experimental results that optogenetic activation of FS induces γ rhythm (Cardin et al.,2009) and the spiking of somatostatin (SOM) and parvalbumin (PV) cells differentially correlates with β and γ oscillations and activation of PV cells enhances γ oscillation in V1 area (Chen et al., 2017).
We computationally identified that γ power can be enhanced by recruiting more high-threshold FS because of the stronger excitation evoked by the cue presentation in WM. This mechanism may be common to other cognitive processes, such as attention and sensory information processing in perceptual decision-making. It has been extensively observed in cognitive processes such as WM (Jensen et al., 2007; Yamamoto et al., 2014), attention (Engel et al., 2001; Fries et al., 2001; Jensen et al., 2007; Fries, 2009; Gregoriou et al., 2009; Kim et al., 2016), and sensory information processing in perceptual decision-making (Frien et al., 2000; Siegel and König, 2003; Kayser and König, 2004; Henrie and Shapley, 2005; Liu and Newsome, 2006; Berens et al., 2008). Studies had observed that the selected neuronal population increased their firing rate when attention was directed to their receptive field (Luck et al., 1997; McAdams and Maunsell, 1999; Bichot et al., 2015; Thiele et al., 2016), implying strong excitation and potentially recruiting more FS neurons. γ oscillations in the sensory cortex are often considered as a proxy for the encoding of sensory evidence during perceptual decision-making because of visual γ band activity and its dependence on stimulus strength and various stimulus features (Frien et al., 2000; Siegel and König, 2003; Kayser and König, 2004; Henrie and Shapley, 2005; Liu and Newsome, 2006; Berens et al., 2008). Based on these, we hypothesize that sensory input would evoke a stronger excitation in the sensory cortex, which could recruit more FS and increase the γ power.
Worth noting that the synapses from pyramidal cells to the nFS are dynamic synapses with short-term potentiation (STP). STP plays an essential role in the modulation of γ and β bursts. In the biological brain, there is short-term synaptic facilitation between pyramidal cells and nFS and short-term synaptic depression between pyramidal cells and FS. These short-term plasticities allow the brain to respond differently to afferents of different durations. Brief currents cause γ oscillations, while prolonged currents cause β oscillations. Based on this mechanism, these observations are reproduced in previously published work (Feng et al., 2021). In our simulation, we only briefly present the stimulus to the model, resulting in oscillations at the γ frequency, and short-term synaptic facilitation between pyramidal cells and nFS helps control excitability in our model. The synapses equipped with STP can temporarily increase inhibition by increasing the effective excitation from the pyramidal cells to the nFS and expanding the range of parameters to keep excitation and inhibition in balance. Too much excitation can lead to the merging of different information (Wei et al., 2012) or false memories (Edin et al., 2009), while too much inhibition can prevent the network from maintaining information. Suppose the synapses from pyramidal cells to the nFS are fixed without STP; the network is prone to produce spurious bursts of activity because of too much excitation or to forget the information because of too much inhibition. At present, we have not considered the effects of prolonged stimulus. It would be interesting to investigate how the brain adapts and responds to prolonged stimulus in the future.
Acknowledgments
Acknowledgements: We thank Prof. XiaoJing Wang for his helpful suggestions.
Footnotes
The authors declare no competing financial interests.
This work was supported by the National Natural Science Foundation of China Grant No. 32171094.
- Received September 9, 2022.
- Revision received October 10, 2023.
- Accepted October 22, 2023.
- Copyright © 2023 Zhao et al.
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