2.1 Sharp transitions from alpha to delta activity

Figure 2(a) shows the one-parameter bifurcation diagram of the Jansen–Rit model (2.1) for varying excitability scaling factor ${A}$ , which determines maximal amplitude of excitatory postsynaptic potentials (EPSP) of excitatory populations (pyramidal cells $Y_1$ ) and excitatory interneurons ( $Y_3$ ). All other parameters are fixed as listed in Table 1. The y-axis shows the PSP of the excitatory interneurons ( $Y_3$ ). A branch of equilibria (blue) folds back and forth in two saddle node bifurcations (purple squares). Solid curves are stable and dashed curves are unstable parts of the branch. Along the upper part of the branch, the equilibria change stability in three Hopf bifurcations (red diamonds). We focus on the periodic orbits (oscillations) emerging from the Hopf bifurcation at a high value of ${A}$ ( ${A}\approx 15$ , label (HB)). The black curves show the maximum and minimum values of $Y_3$ along this branch of mostly stable periodic orbits that emanate from this Hopf bifurcation and terminate at the low- ${A}$ end in a homoclinic bifurcation of saddle-node on invariant circle type (SNIC; [Reference Izhikevich15]), at which the frequency of oscillation goes to zero as ${A}$ approaches the value for the saddle node bifurcation on the lower branch of equilibrium points ( ${A}\approx 7$ ).

Figure 2

(a) Bifurcation diagram of the Jansen–Rit model (2.1) for varying ${A}$ . (b) Frequency of oscillations with input ${p=0}$ (black) and input ${p=120}$ (yellow) for varying ${A}$ , indicating alpha, theta and delta frequency ranges. (c) Same bifurcation diagram as panel (a) but using nondimensionalized quantities $G=B/A$ and $y_3$ . (d),(e) Time profiles of oscillations in alpha ( ${A=11}$ ) and delta ( ${A=10}$ ) rhythm regimes corresponding to vertical dashed lines in panel (b). See Tables 1 and 2 for parameters. Computation performed with coco [Reference Dankowicz and Schilder6].

Figure 2(b) shows the frequency of these oscillations over the range of parameters A where they exist ( ${A}\in [7,14.4]$ ). We observe a sharp transition between high-frequency oscillations for A in the range $[10.2,14.4]$ , where the frequency (in Hz) is in the range $[8,11]$ , and low-frequency oscillation for ${A}$ in the range $[7,10.2]$ , where the frequency is approximately $4$ Hz and below. The oscillation frequencies on either side of the transition are associated with wakefulness state (alpha-like activity, around $10$ Hz) and deep sleep (delta-like activity, around $2$ Hz).

Figures 2(d) and 2(e) show that this change in frequency is accompanied with a qualitative change in the time profiles of the orbits. The alpha-type fast oscillations have a four-phase profile typical for an oscillatory negative feedback loop (compare Figure 2(d) and note that the scale differs between different outputs such that Figure 2(d) has two y-axes):

• • ( $0$ – $0.02$ s) high pyramidal cell output ( $Y_1$ , blue) causes rising inhibition ( $Y_2$ , red);

• • ( $0.02$ – $0.05$ s) high inhibition causes decrease in pyramidal cell output;

• • ( $0.03$ – $0.07$ s) low pyramidal cell output causes decreasing inhibition;

• • ( $0.06$ – $0.1$ s) low inhibition causes rising pyramidal cell output.

Throughout the entire period of the alpha-type oscillation, the pyramidal cell output is supported by a near-constant input from the excitatory interneurons ( $Y_3$ , green). The delta-type (slow) oscillations are shown in Figure 2(e). They show a relaxation-type time profile: pyramidal cell output $Y_1$ (blue) and excitatory interneuron output $Y_3$ (green) collapse to zero and stay there for some period. This period is determined by how long it takes for inhibition ( $Y_2$ , red) to reach sufficiently low levels to permit pyramidal cell output $Y_1$ and excitatory interneuron output $Y_3$ to rise again. The two time profiles in Figures 2(d) and 2(e) occur for parameter values of A just above (Figure 2(d), $A=11$ ) and below (Figure 2(e), $A=10$ ) the transition. The bifurcation diagram shows even a tiny region of bistability, bounded by two saddle-node-of-limit-cycle bifurcations. However, this bistability and the saddle nodes are not a consistent feature for this transition. Figure 2(b) shows a bifurcation diagram for nonzero external input ( $p=120$ Hz, yellow curve), where the transition still occurs and is still sharp, occurring in a small parameter region, but without bistability or saddle nodes of limit cycles.

Forrester et al. [Reference Forrester, Crofts, Sotiropoulos, Coombes and O’Dea11] detected this transition in their investigation of the Jansen–Rit model (2.1). They demonstrated that it is an essential ingredient in the occurrence of large-scale oscillations in a network of neural populations (which would be measurable by EEG). As the transition is not associated with a bifurcation in parts of the parameter space (see Figure 3 of [Reference Forrester, Crofts, Sotiropoulos, Coombes and O’Dea11]), the authors labelled the transition a “false bifurcation” and tracked it in parameter space by associating it with a feature of the time profile, namely the occurrence of an inflection point. Forrester et al. [Reference Forrester, Crofts, Sotiropoulos, Coombes and O’Dea11] pointed out that “false bifurcations” are usually originating from singular perturbation effects in the system, referring to Desroches et al. [Reference Desroches, Krupa and Rodrigues8] and Rodrigues et al. [Reference Rodrigues, Barton, Marten, Kibuuka, Alarcon, Richardson and Terry25].

To investigate this phenomenon further, we nondimensionalize the Jansen–Rit model (2.1) and identify a small parameter $\epsilon $ for which we can then study the singular limit for the transition from alpha to delta activity. The bifurcation diagram in Figure 2(a) shows a sharp change in the time profile of $Y_3$ from nearly constant for alpha activity to an oscillation with excursions close to zero: observe the drop in the minimum of the periodic orbit (black curve) for A slightly above $10$ .

The numerical bifurcation analysis of the Jansen–Rit model is carried out using the coco toolbox [Reference Dankowicz and Schilder6], which is a matlab-based platform for parameter continuation allowing for bifurcation analysis of equilibria and periodic orbits. Numerical continuation in coco [Reference Dankowicz and Schilder6] and xppaut [Reference Ermentrout10] is used to track stability of equilibria and periodic orbits, and to detect their bifurcations. For numerical integration (simulation), we use xppaut and ode45 (a Runge–Kutta method) in matlab. In all single-parameter bifurcation diagrams, solid curves indicate stable states, while dashed lines are unstable states. Black curves indicate maximum and minimum values of periodic solutions in all bifurcation diagramsFootnote ¹.

3.1 Nondimensionalization of the Jansen–Rit model

Touboul et al. [Reference Touboul, Wendling, Chauvel and Faugeras27] present a nondimensionalized version of the Jansen–Rit model. We choose the following dimensionless scale, as proposed by Touboul et al. [Reference Touboul, Wendling, Chauvel and Faugeras27].

• • Dimensionless time is set according to the internal decay time scale of the excitatory populations, $t_{\mathrm {new}}=at_{\mathrm {old}}$ , where ${a}$ is given in Table 1.

• • We introduce the decay rate ratio ${b^*}$ of inhibitory to excitatory populations and the ratio G of postsynaptic amplitudes of inhibitory to excitatory populations:

  $$ \begin{align*} {b^*}=\frac{{b}}{{a}}, \quad G=\frac{{B}}{{A}}. \end{align*} $$

• • We rescale the state variables $Y_1$ , $Y_2$ , $Y_3$ such that they are all of order $1$ at their maximum, introducing a small parameter,

  (3.1) $$ \begin{align}\begin{aligned} &\epsilon= \frac{2{a}}{{Br C(2e_0)}} \approx0.024,\\ &y_1(t_{\mathrm{new}})=rC\epsilon Y_1\bigg(\frac{t_{\mathrm{new}}}{a}\bigg),\quad y_i(t_{\mathrm{new}})= r\epsilon Y_i\bigg(\frac{t_{\mathrm{new}}}{a}\bigg)\quad \text{for }i=2,3.\end{aligned} \end{align} $$

We note that in Figures 2(d) and 2(e), the quantities $Y_1$ and $Y_2$ , $Y_3$ are on different y-axes, because they have different orders of magnitude. The nondimensionalization (3.1) ensures that pyramidal cell output $y_1$ has the same (order- $1$ ) magnitude as the inhibitory interneurons ( $y_2$ ) and the excitatory interneurons ( $y_3$ ). The quantity $b^*$ is a measure of the difference in internal time scales between inhibitory and excitatory populations. Usually, $b^*<1$ as inhibition is slower. The quantity G is a measure for how strong internal feedback strength from inhibitory populations is compared with excitatory populations.

By substituting these dimensionless dependent variables into (2.1) and applying the chain rule with respect to the dimensionless time scale $t_{\mathrm {new}}$ , we obtain the dimensionless form (using and also for the new time)

(3.2)

where the new dimensionless sigmoidal transformation is (see Figure 1(b))

(3.3) $$ \begin{align} {\operatorname{\mathrm{S}}_{\epsilon} (y)}=\cfrac{1}{1+\exp (-y/\epsilon)}. \end{align} $$

As we have rescaled the PSP’s $Y_i(t)$ , the thresholds in the activation function ${\mathrm {S}}$ are now different for each neuron population, such that we call them $y_{0,i}$ . These thresholds $y_{0,i}$ , at which the activation ${\mathrm {S}}_{\epsilon /a_i}(y-y_{0,i})$ equals $1/2$ , now show up in (3.2). The new nondimensional activation thresholds are

(3.4) $$ \begin{align} y_{0,1}&=\frac{ry_0}{a_1}\epsilon,& y_{0,2}&=\frac{ry_0}{a_2}\epsilon,& y_{0,3}&=\frac{ry_0}{a_3}\epsilon, \quad \text{where }a_1=a_3=1, a_2=1/4, \end{align} $$

using the parameters from Table 1, which result in the nondimensional parameters shown in Table 2. Note that the main difference to Touboul’s nondimensionalization [Reference Touboul, Wendling, Chauvel and Faugeras27] is that the definition of the sigmoid function includes the scaling factor $\epsilon $ . Figure 2(c) shows the same bifurcation diagram as shown in Figure 2(a), but uses the nondimensionalized quantities G and $y_3$ . The new primary bifurcation parameter G (the ratio of feedback strengths between inhibition and excitation) is proportional to the inverse of the excitation feedback strength A, such that now, the alpha-to-delta frequency transition occurs for increasing G at $G\approx 2.2$ . The Hopf bifurcation occurs for low $G\approx 1.5$ and the SNIC connecting orbit occurs at $G\approx 3$ .

Table 2

Parameter values of the dimensionless model equation (3.2).

3.2 Discussion of smallness of system parameters

In the nondimensionalized model (3.3), the small parameter $\epsilon $ , which equals $0.024$ , appears in the inverse of the slope of the dimensionless sigmoid ${\mathrm {S}}_\epsilon $ at $y=0$ letting the sigmoid ${\mathrm {S}}_\epsilon $ approach a discontinuous switch in the limit for small $\epsilon $ and $i=1,2,3$ :

(3.5) $$ \begin{align} \lim_{\epsilon \to 0} \operatorname{\mathrm{S}}_{\epsilon} (y-y_{0,i}) &= \begin{cases} 0 & \text{if }y<y_{0,i} , \\ 1 & \text{if }y>y_{0,i}. \end{cases} \end{align} $$

In addition, the activation thresholds $y_{0,i}$ inherit a factor $\epsilon $ in (3.4). However, we observe that there are nontrivial factors in front of $\epsilon $ in several places. Since the $\epsilon $ in the original parameters setting is approximately $1/40$ (so not that small), these factors will influence our strategies for taking the singular limit $\epsilon \to 0$ .

• • The slope in the activation for the inhibitory interneurons, $S_{\epsilon /a_2}$ , is $a_2/(4\epsilon )=1/(16\epsilon )\approx 2.6$ . So, it is further away from the limiting discontinuous switch than the activation of the excitatory populations.

• • The different factors $ry_0/a_1$ , $ry_0/a_3$ and $ry_0/a_2$ of $\epsilon $ in (3.4) are obtained by substituting the baseline values of the original parameters from Table 1 into system (3.2). We observe that the activation thresholds of excitation are small ( $y_{0,1}=y_{0,3}\approx 0.08$ ), but the threshold of inhibition $y_{0,2}$ ( $y_{0,2}\approx 0.3$ ) is not a small quantity.

The smallness of $\epsilon $ expresses that the internal dynamics of neurons in the excitatory populations is fast, leading to small thresholds and steep activation functions after nondimensionalization. The inhibitory neural population is comparatively slower, leading at the population level to a shallower slope of the activation curve and a larger activation threshold [Reference Wilson and Cowan30]. The above observations suggest several possible singular limits.

1. (a) We let $\epsilon $ go to zero in the denominator appearing in ${\mathrm {S}}_{\epsilon /a_i}$ for all neuron populations ( $i=1,2,3$ ) simultaneously. At the same time, we keep the activation thresholds $y_{0,i}$ fixed for all populations. This leaves the parameters $y_{0,i}$ in the model as independent parameters and uses the concrete values from Table 2.

2. (b) We let $\epsilon $ go to zero in the denominator appearing in ${\mathrm {S}}_{\epsilon /a_i}$ for all neuron populations ( $i=1,2,3$ ) simultaneously, and we let the activation thresholds $y_{0,1}$ and $y_{0,3}$ for the excitatory neuron populations go to zero (either proportional to $\epsilon $ or at some lower rate).

3. (c) We let $\epsilon $ go to zero for the excitatory neurons populations ( $i=1,3$ ) in the slope of the activations ${\mathrm {S}}_{\epsilon /a_i}$ , but keep the activation for the inhibition with a fixed finite slope, equal to $\approx 10$ (as well as keeping the activation threshold $y_{0,2}\approx 0.3$ fixed).

We will focus in our analysis on strategy (a) in this paper, because it permits explicit expressions for most equilibria and their bifurcations, and it is sufficient to derive in an implicit algebraic condition for the alpha-to-delta transition. In both limits, the collapse of the alpha-frequency oscillations is a grazing bifurcation [Reference di Bernardo, Budd, Champneys and Kowalczyk9] of periodic orbits in a piecewise linear ODE. In limit (a), the collapse leads to a low excitation equilibrium ( $y_i\ll 1$ for $i=1,2,3$ ), in limit (b), it leads to delta activity.

In the numerical bifurcation diagram in Figure 2(c), the three boundaries for alpha and delta activity are the Hopf bifurcation (onset of alpha frequency oscillations) at $G\approx 1.5$ , the transition between alpha and delta activity at $G\approx 2.2$ , and the connecting orbit to a saddle-node (SNIC bifurcation) at $G\approx 3$ . Two of the boundaries can be found by analysis of equilibria, namely the Hopf bifurcation and the saddle-node bifurcation for small $y_1$ .

For our analysis, the dimensionless external input p is fixed to zero. We use the dimensionless parameter G (the ratio between feedback strength of inhibition versus excitation) as our primary bifurcation parameter. We will later vary $b^*$ , the ratio between internal time scales between inhibition and excitation as a secondary bifurcation parameter.

3.3 Equilibrium analysis in the small- $\epsilon $ limit

Inserting $\epsilon =0$ (or $\epsilon \ll 1$ ) into the activation functions in (3.2) and (3.3) results in an easy-to-interpret limit for equilibria. Setting all derivatives in (3.2) to zero, we obtain that the equilibrium values for $y_1$ , $y_2$ , $y_3$ satisfy the algebraic equations

(3.6) $$ \begin{align} y_1&=\frac{2}{G}\operatorname{\mathrm{S}}_\epsilon(y_3-y_2- y_{0,1}),\end{align} $$

(3.7) $$ \begin{align} y_2&=\frac{2\alpha_4} {b^*}\operatorname{\mathrm{S}}_{\epsilon/a_2}(y_1-y_{0,2}), \end{align} $$

(3.8) $$ \begin{align} y_3&=\frac{2\alpha_2}{G}\operatorname{\mathrm{S}}_\epsilon(y_1-y_{0,3}). \end{align} $$

The right-hand sides of (3.7) and (3.8) define the equilibrium values $y_2$ and $y_3$ as a function of $y_1$ . This is also true in the limit $\epsilon =0$ , whenever $y_1$ is not on the activation threshold ( $y_{0,2}$ and $y_{0,3}$ , respectively).

Inserting (3.7) and (3.8) into (3.6), and taking the Heaviside limit $S_0$ in (3.5) for the activation functions $S_{\epsilon /a_i}$ (indicating the dependence of $y_2$ and $y_3$ on $y_1$ ), leads to the relation

(3.9) $$ \begin{align} \frac{G}{2}y_1 =\operatorname{\mathrm{Rhs}}_0(y_1):= \begin{cases} 0 & \text{if }y_3(y_1-y_{03})-y_2(y_1-y_{02})< y_{01}, \\ 1& \text{if } y_3(y_1-y_{03})-y_2(y_1-y_{02})> y_{01}. \end{cases} \end{align} $$

Analysis of (3.6)–(3.8) in the limit $\epsilon \to 0$ is supported by Figure 3. Each panel shows the left-hand side of (3.9) (in red) and the right-hand side ${\mathrm {Rhs}}_0(y_1)$ of (3.9) (in blue) as a function of $y_1$ for different values of G, otherwise using the parameter values in Table 2. The (red) left-side function is the straight line $y_1\mapsto Gy_1/2$ . The right-side function ${\mathrm {Rhs}}_0(y_1)$ is piecewise constant. Defining two critical values for parameter G as

(3.10) $$ \begin{align} G_1=\frac{\alpha_2b^*}{\alpha_4+y_{0,1}b^*/2}\approx1.48,\quad G_3=\frac{2\alpha_2}{y_{0,1}}\approx19.4, \end{align} $$

(thus, $0<G_1<G_3$ ) the right-hand side equals (recall that $0<y_{0,3}<y_{0,2}$ )

$$ \begin{align*} \begin{aligned} \\ \operatorname{\mathrm{Rhs}}_0(y_1)&= \begin{cases} \\ \\ \ \end{cases} \end{aligned}\hspace{-2em} \begin{aligned} &&\text{for }G\text{ in:}&&(0,&G_1),& (G_1,&G_3),&(G_3,&\infty),\\ y_1&\in(0,y_{0,3})& \text{(}y_2\text{ off}, y_3\text{ off):}&& &0 &&0 &0\\ y_1&\in(y_{0,3},y_{0,2})& \text{(}y_2\text{ off}, y_3\text{ on):} && &1 &&1 &0\\ y_1&\in(y_{0,2},\infty)& \text{(}y_2\text{ on}, y_3\text{ on):} && &1 &&0 & 0 &.\\ \end{aligned} \end{align*} $$

Each intersection of the two lines in Figure 3 is a stable equilibrium if ${\mathrm {Rhs}}_0$ (blue line) is horizontal in the intersection, or an unstable, so-called pseudo-equilibrium [Reference di Bernardo, Budd, Champneys and Kowalczyk9, Reference Kuznetsov, Rinaldi and Gragnani20], if ${\mathrm {Rhs}}_0$ is vertical in the intersection. For $\epsilon \ll 1$ but nonzero, the pseudo-equilibria of the piecewise linear limit system will become unstable equilibria with strongly unstable eigenspaces. Table 3 lists the locations of equilibria for all ranges of parameter G, with two additional critical values for G,

(3.11) $$ \begin{align} G_2=\frac{2}{y_{0,2}}\approx6.3, \quad G_4=\frac{2}{y_{0,3}}\approx24.8. \end{align} $$

Figure 3(h) shows the resulting bifurcation diagram in the limit of small $\epsilon $ .

Figure 3

(a)–(g) Intersections between left-hand (red) and right-hand (blue) sides of (3.9) for different bifurcation parameters G (plots used $\epsilon =0.001$ , $y_{0,1},y_{0,3}=0.08$ and $y_{0,2}=0.3$ ). (h) Numerical bifurcation diagram of equilibria in $(G,y_1)$ -plane obtained by coco [Reference Dankowicz and Schilder6]. ${\mathrm {S}}_\epsilon /a_1={\mathrm {S}}_\epsilon (y_3-y_2- y_{0,1})$ for $y_1$ in (3.6). For other parameters, see Table 2.

Table 3

Equilibrium points present in system (3.2) for all parameter ranges of G: for critical values of G, see (3.10), (3.11). The index is the number of unstable eigenvalues of the Jacobian in the equilibrium for nonzero $\epsilon $ .

Figure 3(a) shows that for $G<G_1$ , we have a pair of stable and unstable equilibria, both with small values of $y_1$ : the unstable equilibrium is a saddle with $y_1=y_{0,3}=0.08$ for $0<\epsilon \ll 1$ , and the stable equilibrium is a node with a $y_1$ -value very close to zero, of the order $\exp (-y_{0,1}/\epsilon )$ .

There exists another stable equilibrium, for which $y_1$ is large, equal to $2/G$ . At $G=G_1$ (Figure 3(b), see (3.10) for value of $G_1$ ), ${\mathrm {Rhs}}_0$ drops to zero for $y>y_{0,2}$ , which results in a sudden drop of the $y_1$ -component of the large- $y_1$ equilibrium to $y_{0,2}$ and its loss of stability. For positive $\epsilon $ , this loss of stability is a Hopf bifurcation (see the labelled point HB1 in Figure 3(h)). Figure 3(c) shows the equilibria for $G\in (G_1,G_2)$ . Figure 3(d) shows the situation at the next critical value of G, the Hopf bifurcation $G=G_2=2/y_{0,2}$ , when the left-hand side function (red line) goes through the “corner” of ${\mathrm {Rhs}}_0$ at $y_1=y_{0,2}$ (see red diamond). At this value, the equilibrium with $y_1=y_{0,2}$ regains its stability in a Hopf bifurcation for nonzero $\epsilon $ (see the labelled point HB2 in Figure 3(h)). Figure 3(e) shows the situation for $G>G_2$ with two stable equilibria and one saddle. The next critical G is $\min (G_3,G_4)$ , when either the excitatory interneuron activity is no longer sufficient to overcome the threshold ( $G_3$ , when the blue line in Figure 3(g) drops to zero on the $y_1$ -interval $(y_{0,3},y_{0,2})$ ), or the left-hand side (red curve) touches the “corner” of ${\mathrm {Rhs}}_0$ at $y_1=y_{0,3}$ ( $G_4$ ). In both cases, the resulting equilibrium bifurcation is a saddle-node bifurcation of the equilibrium with $y_1=y_{0,1}=y_{0,3}$ (see the labelled SN in Figure 3(h)). For the parameters we study, we have that $G_3<G_4$ such that $G_4$ does not change the dynamics.

3.4 Brief comment on the excitatory activation thresholds $y_{0,1}$ and $y_{0,3}$

While we will treat the excitatory thresholds $y_{0,1}$ and $y_{0,3}$ as positive constants in our analysis in Section 3.5 (called strategy (a) in Section 3.2), let us perform a quick check of what happens if we also assume that the excitatory thresholds $y_{0,1}$ and $y_{0,3}$ are assumed to be small. If we assume proportionally small excitatory activation thresholds $y_{01}=y_{03}\sim \epsilon $ , then the saddle and node equilibria with $y_1\approx 0$ are no longer present for any G of order $1$ . This follows immediately from a perturbation analysis of the equilibria for small nonzero $\epsilon $ and excitatory activation thresholds of the form

$$ \begin{align*} y_{0,1}&=\epsilon z_{0,1},\, y_{0,3}=\epsilon z_{0,3}, \, z_{0,1},z_{0,3}>0, \, z_{0,1},z_{0,3}=O(1). \end{align*} $$

Let us assume that $y_{1,\mathrm {eq}}\ll 1$ , in particular, $y_{1,\mathrm {eq}}\ll y_{0,2}$ , and check that no such equilibrium can exist. The smallness of $y_{1,\mathrm {eq}}$ together with identity (3.7),

$$ \begin{align*}y_{2,\mathrm{eq}}=\frac{2\alpha_4}{b^*\big\{1+\exp((y_{0,2}-y_{1,\mathrm{eq}})/(\epsilon/a_2))\big\}},\end{align*} $$

would imply that the corresponding inhibitory interneuron activity $y_{2,\mathrm {eq}}$ is very close to zero: $y_{2,\mathrm {eq}}\sim \exp (-a_2 y_{0,2}/\epsilon )\leq \epsilon $ . From this, the identities (3.6) and (3.8) imply positive lower bounds independent of $\epsilon $ for the equilibrium pyramidal-cell activity $y_{1,\mathrm {eq}}$ and the excitatory interneuron activity $y_{3,\mathrm {eq}}$ , namely,

$$ \begin{align*} \begin{split} y_{1,\mathrm{eq}}&\geq\frac{2}{G}\frac{1}{1+\exp(z_{0,1}+y_{2,\mathrm{eq}}/\epsilon)}\geq \frac{2}{G}\frac{1}{1+\exp(z_{0,1}+1)},\\ y_{3,\mathrm{eq}}&\geq\frac{2\alpha_2}{G}\frac{1}{1+\exp(z_{0,3})}. \end{split} \end{align*} $$

Hence, for excitatory activation thresholds of order $\epsilon $ , the pair of small- $y_1$ equilibria does not exist. The other equilibria in Table 3 and Figure 3(h) have well-defined limits for $y_{0,1}, y_{0,3}\to 0$ : the stable equilibrium has the limit $y_1=2/G$ , the unstable equilibrium has the limit $y_1=y_{0,2}$ and its inhibitory activity $y_2$ approaches $2\alpha _2/G$ for small $y_{0,1}$ . The saddle-node bifurcation at $\min (G_3,G_4)$ goes to infinity as $y_{0,1},y_{0,3}\to 0$ , such that the large-activity equilibrium exists over a wider range of G. This leaves the region $(G_1,G_2)$ between the two Hopf bifurcations, where no stable equilibrium exists for $y_{0,1},y_{0,3}$ of order $\epsilon $ . The Hopf bifurcation at $G_1$ has the limit $\alpha _2b^*/\alpha _4$ for $y_{0,1}\to 0$ , while $G_2$ is independent of $y_{0,1}$ and $y_{0,3}$ .

3.5 Periodic orbits in the small- $\epsilon $ limit

3.5.1 The case $\epsilon =0$ as a piecewise smooth ODE

System (3.2) in the limit $\epsilon \to 0$ has discontinuities on the right-hand side such that it is only piecewise smooth in its phase space [Reference di Bernardo, Budd, Champneys and Kowalczyk9]. For such systems, there is, in general, no guarantee that trajectories can be consistently continued across discontinuities by following the flow defined by the right-hand sides in each part of the phase space. The problem can be illustrated for the simplest scenario, an ODE with a right-hand side consisting of two smooth pieces and a single discontinuity,

with smooth right-hand sides $f_\pm :{\mathbb {R}}^n\to {\mathbb {R}}^n$ and switching function $h:{\mathbb {R}}^n\to {\mathbb {R}}$ (for example, consider the scalar example , where $f_\pm (x)=\mp 1$ and $h(x)=x$ ). For all points with $\pm h(x)>0$ , it is clear that the trajectory through x is determined by $f_\pm (x)$ . However, when following a trajectory $x(t)$ , one may approach a point x with $h(x)=0$ (on the discontinuity surface $\{x\mid h(x)=0\}$ ). If, in this point x, the quantities $h'(x)f_-(x)$ and $ h'(x)f_+(x)$ have opposite sign (using $h'(x)$ for $\partial h(x)$ ), then the trajectory cannot “cross to the other side” of the discontinuity surface ${D=\{x\mid h(x)=0\}}$ consistent with the right-hand sides. A commonly adopted convention for this case where the flows on both sides of a codimension- $1$ discontinuity surface point in opposite directions relative to the surface, is continuing the trajectory using the Filippov solution, or sliding, by following the flow of

However, the structure of (3.2) rules out this common scenario of sliding, where $h'(x)f_+(x)h'(x)f_-(x)<0$ , for the discontinuity surfaces in system (3.2) generated by the limit $\epsilon \to 0$ in (3.3). System (3.2) has three hyperplanes where it is discontinuous in the limit $\epsilon \to 0$ , with the switching functions

In each of the hyperplanes $\{h_i=0\}$ , where the vector field is discontinuous, the two half-space vector fields point into the same direction relative to the hyperplane. This is due to the presence of inertia: (3.2) is a system of second-order equations for the firing activities, but the switches in ${\mathrm {S}}_0$ depend only on the firing activities, not their time derivatives. Let us inspect each codimension- $1$ discontinuity set.

• • $D_i{\kern-1pt}={\kern-1pt}\{y{\kern-1pt}\mid{\kern-1pt} y_1{\kern-1pt}={\kern-1pt}y_{0,i}\}$ for $i{\kern-1pt}={\kern-1pt}2,3$ , such that the switching function is : the inner product of the vector fields on both sides of $D_i$ with the gradient of $h_i$ equals , which is continuous across $D_i$ and, thus, all nonequilibrium trajectories cross the surface $D_i$ in the same direction from both sides of $D_i$ .

• • $D_1{\kern-1pt}={\kern-1pt}\{y{\kern-1pt}\mid{\kern-1pt} y_3-y_2{\kern-1pt}={\kern-1pt}y_{0,1}\}$ , such that the switching function is : the inner product of on both sides of $D_1$ is , which is also continuous across $D_1$ .

Hence, the set $\mathcal {D}$ in phase space where trajectories are not well defined in the classical sense is much smaller than for common piecewise smooth ODEs. It is a subset of the codimension- $2$ surfaces for $i=2,3$ and . Examples of points in these sets $\mathcal {D}$ are the “unstable equilibria” $y_{\mathrm {eq}_2}$ listed in Table 3. These types of equilibria are called pseudo equilibria in the literature about Filippov systems. As the equilibrium discussion in Figure 3 shows, the pseudo equilibria for $\epsilon =0$ are limits of unstable equilibria for $\epsilon>0$ .

3.5.2 Numerical bifurcation analysis of $\alpha $ -type periodic orbits for small positive $\epsilon $

As seen in Table 3 and Figure 3(h), in the small- $\epsilon $ limit, the high-activity equilibrium $y_1$ changes not only its stability at $G=G_1$ but also its location: the equilibrium pyramidal-cell activity drops sharply from $y_1=2/G=2/G_1\approx 1.35$ to $y_1=y_{0,2}\approx 0.3$ . Hence, in the singular limit, we cannot expect the standard Hopf bifurcation scenario with a family of nearly harmonic small-amplitude periodic orbits branching off.

We initially perform a numerical continuation for the full nondimensionalized system (3.2) for small $\epsilon $ ( $\epsilon =0.001$ ). Figure 4 zooms into the range of parameters G where periodic orbits of $\alpha $ type exist, near the Hopf bifurcation at $G=G_1\approx 1.48$ (see (3.10)). We observe that the pyramidal-cell ( $y_1$ ) amplitude for the family of periodic orbits emerging from the Hopf bifurcation grows over a small range of G to order $1$ (black curve in top panel in Figure 4), by approximately the same amount as the equilibrium value of $y_1$ dropped before the Hopf bifurcation. This explosive growth in amplitude is reminiscent of canard explosions as first described for the classical van der Pol or FitzHugh–Nagumo oscillator by Benoît [Reference Benoît, Callot, Diener and Diener2] (see Krupa and Szmolyan [Reference Krupa and Szmolyan18] for general theory and Wechselberger [Reference Wechselberger28] for a review). However, the inhibition amplitude $y_2$ grows square-root-like with $G-G_1$ , as occurs at a classical Hopf bifurcation. This discrepancy and the fact that the singular limit $\epsilon \to 0$ is not slow–fast but discontinuous suggests that another mechanism must be responsible for the explosive growth in amplitude of $y_1$ . Exploration of this phenomenon is beyond the scope of this paper. The time profile of periodic orbits near the explosion is shown in Figure 7 in the discussion in Section 4.

Figure 4

Equilibria and periodic orbits branching off Hopf bifurcation (HB1) for system (3.2) for varying G and small $\epsilon =0.001$ , showing equilibrium values, or maxima and minima of the neural activities $y_1,y_2,y_3$ , respectively. Green vertical line corresponds to canard orbit shown in Figure 7. Grey vertical line is at parameter $G=1.7$ , used in singular limit in Figure 5(a). Other parameters are listed in Table 2.

Figure 5

Time profile of alpha-type piecewise exponential periodic orbits given in (3.15), representing solutions of the piecewise linear ODE (3.12), (3.13) for $y_{0,1},y_{0,3}=0.08$ , $y_{0,2}=0.3$ and $y_3$ fixed at its equilibrium value $2\alpha _2/G$ . (a) Orbit for $(b^*,G)=(0.5,1.7)$ ; $t_{\mathrm {s_2,off}}$ , $t_{\mathrm {s_2,on}}$ : threshold crossing times of $y_1$ (where $y_1=y_{0,2}$ ); $t_{\mathrm {s_1,off}}$ , $t_{\mathrm {s_1,on}}$ : threshold crossing times of $y_2$ (where $y_2=y_3-y_{0,1}$ ). Dashed blue line (legend entry $S_0$ (pc)) is activation switch ${\mathrm {S}}_{0}(y_3-y_2(t)-y_{0,1})$ for $y_1$ with $\epsilon =0.001$ . Dashed red line (legend entry $S_0$ (inh)) is activation switch ${\mathrm {S}}_{0}(y_1(t)-y_{0,2})$ for $y_2$ . (b) Same as panel (a) but for $(b^*,G)=(0.44,1.7)$ , where orbit grazes ( $y_{1,\min }=y_{0,3}$ , green star on grey horizontal line $\{y=y_{0,3}\}$ ). For other parameters, see Table 2.

3.5.3 Piecewise linear oscillations in a negative feedback loop between pyramidal cells and inhibition for $\epsilon =0$

In Figure 4, we also note that for $G\approx G_1$ and pyramidal-cell activity $y_1$ in the range between $y_{0,2}$ and $2/G_1$ , the excitatory interneurons are uniformly active, as $y_1$ is always above the threshold $y_{0,3}$ at which excitatory interneuron activity is switched off, and which is much smaller than $y_{0,2}$ . Hence, $y_3$ will be positive and at its equilibrium value

$$ \begin{align*} y_3(t)\approx y_{3,\mathrm{eq}}=\frac{2\alpha_2}{G}\approx \frac{2\alpha_2}{G_1}= y_{0,1}+\frac{2\alpha_4}{b^*}. \end{align*} $$

Thus, we may replace $y_3$ in the sigmoid input for $y_1$ with its equilibrium value, resulting in the four-dimensional system for pyramidal-cell and inhibitory activity only, inserting the limit $\epsilon =0$ :

(3.12)

(3.13)

System (3.12), (3.13) is a classical negative feedback loop with inertia: $y_2$ suppresses $y_1$ , while $y_1$ promotes $y_2$ . Each oscillation period has four phases and follows a piecewise linear second-order vector field in each of the phases.

Figure 5(a) shows the typical shape for the $y_1$ and $y_2$ profile of the oscillations. We reduce the periodic problem for the four-dimensional piecewise affine ODE to a four-dimensional algebraic problem, parametrizing the oscillations using the times at which the respective activations $u_1, u_2$ switch between $0$ and $1$ (without loss of generality $t_{\mathrm {s_1,off}}=0$ ), (see Figure 5(a) for guidance):

$$ \begin{align*} \begin{aligned} t_{\mathrm{s_1,off}}\phantom{:}\ &=0<t_{\mathrm{s_2,off}}<t_{\mathrm{s_1,on}}<t_{\mathrm{s_2,on}}<T,\quad \text{where}\\ t_{\mathrm{s_1,off}}:&\ y_2\text{ crosses }2\alpha_2/G-y_{0,1}\text{ from below to above,}&&\text{(}u_1:1\to0\text{),}\\ t_{\mathrm{s_2,off}}:&\ y_1\text{ crosses }y_{0,2}\text{ from above to below,}&&\text{(}u_2:1\to0\text{),}\\ t_{\mathrm{s_1,on}}:&\ y_2\text{ crosses }2\alpha_2/G-y_{0,1}\text{ from above to below,}&&\text{(}u_1:0\to1\text{),}\\ t_{\mathrm{s_2,on}}:&\ y_1\text{ crosses }y_{0,2}\text{ from below to above,}&&\text{(}u_2:0\to1\text{),}\\ T:&\ \text{period of oscillation.} \end{aligned} \end{align*} $$

3.5.4 Alpha-type oscillations in the singular limit

For $G>G_1$ , the periodic orbits consist of two coupled pairs of orbit segments, one pair of segments for pyramidal activity $y_1$ governed by the piecewise linear ODE (3.12) (switching at times $t_{\mathrm {s_1,off}}$ and $t_{\mathrm {s_1,on}})$ and one pair of segments for inhibition $y_2$ governed by the piecewise linear ODE (3.13) (switching at times $t_{\mathrm {s_2,off}}$ and $t_{\mathrm {s_2,on}}$ ), as shown in Figure 5(a). The coupling occurs through the switching times, as the switching times $t_{\mathrm {s_2,on/off}}$ are determined by $y_1$ crossing the threshold $y_{0,2}$ , and the switching times $t_{\mathrm {s_1,on/off}}$ are determined by $y_2$ crossing the threshold $y_{\mathrm {3,eq}}-y_{0,1}=2\alpha _2/G-y_{0,1}$ . Both piecewise linear ODEs (3.12) and (3.13) are affine ODEs of the form

(3.14)

with inhomogeneity c. The general solution of (3.14) for its IVP with initial condition is

$$ \begin{align*} \text{where} \quad e^{M_bt}&=e^{-bt}\begin{bmatrix} 1+bt&t\\ -b^2t&1-bt \end{bmatrix}, \end{align*} $$

$e^{(\cdot )}$ is the exponential and $\mathbf {{e}}_1=(1,0)^{\mathsf {T}}$ is the first standard basis vector. From this general formula, we find that requiring periodicity of the pyramidal activity $y_1$ and the inhibition $y_2$ determines their initial conditions at the “off” switching time. The initial conditions

where $Y_{\mathrm {per}}$ is defined as

$$ \begin{align*}Y_{\mathrm{per}}(T,\delta_t;b)=[I_2-e^{M_bT}]^{-1}[I_2-e^{M_b(T-\delta_t)}]\mathbf{{e}}_1,\end{align*} $$

will lead to a solution of (3.12), (3.13) that is periodic with period T. The switching times $t_{\mathrm {s_1,on}}$ , $t_{\mathrm {s_2,on/off}}$ and the period T satisfy four relations that follow from the crossing conditions $y_1( t_{\mathrm {s_2,off}})=y_1( t_{\mathrm {s_2,on}})=y_{02}$ and $y_2( t_{\mathrm {s_1,off}})=y_2( t_{\mathrm {s_1,on}})=y_3-y_{01}$ :

(3.15) $$ \begin{align} \begin{aligned} y_{0,2}&=\mathbf{{e}}_1^{\mathsf{T}} Y_{\mathrm{adv}}(t_{\mathrm{s_2,off}}; 1,0, (\cdot))\circ\frac{2}{G}Y_{\mathrm{per}}(T,t_{\mathrm{s_1,on}}\!-t_{\mathrm{s_1,off}};1),\\[0.1cm] y_{0,2}&=\mathbf{{e}}_1^{\mathsf{T}} Y_{\mathrm{adv}}(t_{\mathrm{s_2,on}}-T;1,2/G, (\cdot))\circ\frac{2}{G}Y_{\mathrm{per}}(T,t_{\mathrm{s_1,on}}\!-t_{\mathrm{s_1,off}};1),\\[0.1cm] \frac{2\alpha_2}{G}-y_{0,1}&=\mathbf{{e}}_1^{\mathsf{T}} Y_{\mathrm{adv}}(t_{\mathrm{s_1,on}}-t_{\mathrm{s_2,off}};b^*,0,(\cdot))\circ\frac{2\alpha_4}{b^*} Y_{\mathrm{per}}(T,t_{\mathrm{s_2,on}}\!-t_{\mathrm{s_2,off}};b^*),\\[0.1cm] \frac{2\alpha_2}{G}-y_{0,1}&=\mathbf{{e}}_1^{\mathsf{T}} Y_{\mathrm{adv}}(-t_{\mathrm{s_2,off}};b^*,2\alpha_4/b^*,(\cdot))\circ\frac{2\alpha_4}{b^*} Y_{\mathrm{per}}(T,t_{\mathrm{s_2,on}}\!-t_{\mathrm{s_2,off}};b^*). \end{aligned} \end{align} $$

In (3.15) and below, we use the composition symbol $\circ $ with the meaning $f\circ x=f(x)$ , $f\circ g\circ x=f(g(x))$ and so forth to reduce nesting of brackets. Recall that we set $t_{\mathrm {s_1,off}}=0$ such that this switching time has been omitted in the defining equations (3.15). We have already inserted the appropriate initial conditions to ensure periodicity with period T into (3.15), such that the system (3.15) is a system of four algebraic equations, depending on the four unknown times $t_{\mathrm {s_1,on}}$ , $t_{\mathrm {s_2,off}}$ , $t_{\mathrm {s_2,on}}$ and T, and system parameters such as G, $b^*$ and the switching thresholds $y_{0,1},y_{0,2}$ . For given switching times and period, one may obtain the complete periodic orbit as

Figure 5(a) shows an example of such a piecewise exponential and its switching times. The associated thresholds $y_{\mathrm {3,eq}}-y_{0,1}=2\alpha _2/G-y_{0,1}$ and $y_{0,2}$ for $y_2(t)$ and $y_1(t)$ for the switching events of $y_1(t)$ and $y_2(t)$ are shown as magenta and yellow horizontal lines, respectively.

3.5.5 Detection of grazing boundary for alpha activity

The orbits given by the algebraic system (3.15) are alpha-type oscillations with constant support by excitatory interneuron population $y_3$ . A criterion for the existence of these oscillations is that $y_1$ never crosses below the threshold $y_{0,3}$ at which the excitatory interneurons $y_3$ switch off. The piecewise exponential has a distinct minimum $y_{1,\min }$ at some time $t_{1,\min }$ , labelled by a star in Figure 5(a). This minimum consistently occurs shortly after $t_{\mathrm {s_1,on}}$ , in the interval between $t_{\mathrm {s_1,on}}$ and $t_{\mathrm {s_2,on}}$ , due to the inertia caused by the second-order nature of the ODE for $y_1$ . Hence, we may introduce $y_{1,\min }$ and $t_{1,\min }$ as additional parameters satisfying the relations

(3.16) $$ \begin{align} \begin{bmatrix} y_{1,\min}\\0 \end{bmatrix}&= Y_{\mathrm{adv}}(t_{1,\min}-t_{\mathrm{s_1,on}};1,2/G,(\cdot))\circ Y_{\mathrm{adv}}(t_{\mathrm{s_1,on}};1,0,(\cdot)) \notag \\ &\quad \circ (2/G)Y_{\mathrm{per}}(T,t_{\mathrm{s_1,on}}-t_{\mathrm{s_1,off}};1). \end{align} $$

We add the two parameters $t_{1,\min }$ and $y_{1,\min }$ while tracking solutions of (3.15), (3.16) in system parameters to monitor the difference $y_{1,\min }-y_{0,3}$ for roots. The difference equals the distance between the point on the graph in Figure 5(a) labelled with a star and the grey horizontal line $D_3=\{y_1=y_{0,3}\}$ . When this difference is zero, we have a grazing bifurcation: the alpha activity oscillation’s orbit touches the threshold $D_3=\{y\mid y_1=y_{0,3}\}$ in a quadratic tangency. To find an orbit with such a quadratic tangency, we fix $G=1.7$ and vary the time scale ratio $b^*$ from $b^*=0.5$ downwards along the horizontal dashed line shown in Figure 6(a). While doing so, we monitor the quantity $y_{1,\min }-y_{0,3}$ until it reaches zero, which is the point that represents the grazing bifurcation. The grazing occurs at $(b^*,G)\approx (0.44,1.7)$ , labelled with a green star in Figure 6(a).

Figure 6

(a) Composite bifurcation diagram overlaying regions of alpha and delta activity of system (3.2) for $\epsilon =0.024$ , and the grazing bifurcation (black curve) detected by solving algebraic equations (3.15), (3.16) in the $(b^*,G)$ parameter plane. (b,c) Time profiles of typical alpha activity ( $(b^*,G)=(0.4,1.4)$ , blue circle in panel (a)) and delta activity ( $(b^*,G)=(0.4,1.6)$ , yellow circle in panel (a)) for each neuron population at $\epsilon =0.024$ . Folds of periodic orbit (SNP, two purple curves on top of each other) and Hopf bifurcation (HB, red) are for $\epsilon =0.024$ , SNIC bifurcations are shown for $\epsilon =0.024,0.015,0.01$ . Horizontal grey dashed line for $b^*\in [0.44,0.5]$ at $G=1.7$ is the parameter path for continuation to grazing bifurcation reached at the point labelled by a green star. See Table 2 for other parameters.

4.1 Canard explosion of alpha-type orbits at the singular Hopf bifurcation

The Hopf bifurcation, shown in the zoomed-in bifurcation diagram in Figure 4 can be studied in the singular limit $\epsilon =0$ . Figure 7 shows that the periodic orbits in this canard-like explosion are parametrized by the time during which inhibition is switched off (drop of dashed curve in Figure 7(b) to zero), which is short for values of the parameter G close to the bifurcation value $G_1$ . Since the periodic orbits are piecewise exponentials, the asymptotic parameter-amplitude dependence for $G\approx G_1$ can be determined explicitly.

4.2 Improved limit for slow inhibition

The small thresholds $y_{0,1}$ and $y_{0,3}$ and sharp sigmoid slopes in the activation function ${\mathrm {S}}_\epsilon $ model the fact that excitatory populations have faster internal dynamics. We kept the threshold for the inhibition $y_{0,2}$ nonsmall, but replaced the activation function of the inhibition ${\mathrm {S}}_{\epsilon /a_2}$ also by an all-or-nothing switch in the limit $\epsilon \to 0$ . A more appropriate limit consideration would be to keep a finite-slope sigmoid activation function ${\mathrm {S}}_2(y_1-y_{0,2})$ in place for the inhibition and take the small- $\epsilon $ limit only for the excitatory populations. We would still encounter the same dynamical phenomenon for the alpha–delta transition in the limit, namely a grazing bifurcation. However, the limiting periodic orbits of alpha type would no longer be determined by an algebraic system with exponentials, such as (3.15), but as solutions of a nonlinear ODE over a finite time interval. So, finding the grazing bifurcation would require solving a nonlinear ODE numerically, instead of using the formulae in (3.15) and (3.16).

Figure 7

Time profile for alpha-type canard orbit near Hopf bifurcation (labelled HB1, in Figure 4) and activations ${\mathrm {S}}_{\epsilon /a1}$ for $y_1$ , and ${\mathrm {S}}_{\epsilon /a2}$ for $y_2$ . Parameters: $G=1.49\approx G_1=1.48$ (green dashed vertical line in Figure 4 near HB1), $\epsilon =0.001,y_{0,1},y_{0,3}=0.08$ and $y_{02}=0.3$ , for others, see Table 2. Inset in panel (b) shows that $y_2$ crosses the threshold $y_{\mathrm {3,eq}}-y_{0,1}$ of $y_1$ .

4.3 Dependence of delta-type oscillations on parameters including nonzero input

Delta-type oscillations in the zero-input case studied here have excitation population potentials $y_1$ and $y_3$ at zero until the inhibition $y_2$ drops sufficiently low such that $y_1$ and $y_3$ can recover and generate a large-amplitude burst (see Figure 2(e)). The precise time ratios between time spent at zero and bursts depends on time scale ratios such as $b^*$ , and thresholds. A positive external input p will affect the time profile and frequency of delta-type oscillations, as positive p will prevent $y_3$ from staying near zero, resulting in a positive slope at low potential values, as seen in the simulations done by Forrester et al. [Reference Forrester, Crofts, Sotiropoulos, Coombes and O’Dea11] and Ahmadizadeh et al. [Reference Ahmadizadeh, Karoly, Nešić, Grayden, Cook, Soudry and Freestone1].