Firing patterns in the adaptive exponential integrate-and-fire model
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Abstract
For simulations of large spiking neuron networks, an accurate, simple and versatile single-neuron modeling framework is required. Here we explore the versatility of a simple two-equation model: the adaptive exponential integrate-and-fire neuron. We show that this model generates multiple firing patterns depending on the choice of parameter values, and present a phase diagram describing the transition from one firing type to another. We give an analytical criterion to distinguish between continuous adaption, initial bursting, regular bursting and two types of tonic spiking. Also, we report that the deterministic model is capable of producing irregular spiking when stimulated with constant current, indicating low-dimensional chaos. Lastly, the simple model is fitted to real experiments of cortical neurons under step current stimulation. The results provide support for the suitability of simple models such as the adaptive exponential integrate-and-fire neuron for large network simulations.
Keywords
Simplified single-neuron models Firing patterns Chaos Electrophysiological taxonomy1 1 Introduction
Large-scale simulations of cortical activity and theoretical investigations of neuronal dynamics require neuron models that are mathematically tractable, biologically relevant and computationally fast. Moreover, a modeling framework should be sufficiently versatile to span the whole diversity of neuron types by tuning a restricted number of parameters, avoiding the need of a new model for each class of neuron. Modeling the complete gating dynamics of ion channel densities in neuronal membranes satisfies only two of these five requirements: biological relevance and versatility. On the other hand, modeling a neuron either as a coincidence detector, as a resettable integrator or as a stochastic point process may fail to catch important aspects of single-neuron behavior. Small modifications to these simple models can bring them closer to reality. Lately multiple advances were made in that direction.
In the presence of high synaptic bombardment, modeling accurately the spike initiation is crucial and the Leaky Integrate-and-Fire (LIF) must be augmented by an exponential term to faithfully process fast inputs signals (Fourcaud-Trocme et al. 2003). An additional recovery variable is important to capture adaptation and resonance properties (Richardson et al. 2003; Izhikevich 2003). A simple quadratic model of spike initiation with a linearly dependent recovery variable and a reset in the state variables is sufficient to account for most types of firing patterns observed in the central nervous system (Izhikevich 2007), but spike firing in that model occurs with an unrealistic delay. Unlike a quadratic dependence on voltage (Latham et al. 2000), an exponential nonlinearity (Fourcaud-Trocme et al. 2003) keeps the subthreshold dynamics linear and matches direct measurements in cortical neurons (Badel et al. 2007). The exponential model combined with an adaptation variable, called the adaptive exponential integrate-and-fire (AdEx),1 is simple because it is described by only two equations and a reset condition. It is by construction more realistic than the LIF, and it was shown to predict with high accuracy the spike timing of a conductance-based Hodgkin and Huxley model (Brette and Gerstner 2005) and the spike timing of real pyramidal neurons under noisy current injection ({x8|Clopath et al. 2007}; Jolivet et al. 2007).
In this paper, we explore the versatility and the biological relevance of the AdEx model. We show that the AdEx reproduces multiple firing patterns and study the correspondence between the parameters and the firing types. Finally, we fit the model to experimental traces and obtain sets of parameters describing cortical fast spiking interneurons and regular spiking pyramidal neurons.
2 2 Adaptive exponential integrate-and-fire
We emphasize that voltage reset is to a fixed value V_{r}, whereas w-reset is by a fixed amount b. Therefore the adaptation variable w can accumulate during a spike train whereas voltage does not.
There are nine parameters required to define the evolution of the membrane potential (V) and the adaptation current (w). The nine parameters can be separated into scaling parameters and bifurcation parameters. The scaling parameters are the parameters responsible for scaling the time axis, for the stretch and for the offset of state variables. The five scaling parameters are: total capacitance (C), total leak conductance (g_{L}), effective rest potential (E_{L}), threshold slope factor (Δ_{T}), effective threshold potential (V_{T}). Absorbing the parameters C and g_{L} into the time scale τ_{m} = C/g_{L} , and using Δ_{T} and V_{T} to set the scale and offset of the membrane potential, then after appropriate rescaling of I and w, Eqs. 1 and 2 can be reduced to a system of equations with dimensionless variables and only four parameters (Touboul 2008). The resulting four parameters are bifurcation parameters and are directly proportional to the conductance a, the time constant τ_{w} the spike triggered adaptation b, and the reset potential V_{r}. Modifying these parameters brings qualitative changes in the behavior of the system, such that different firing patterns become possible. Note that a controls the sensitivity of the adaptation current to voltage even in the absence of spikes. This voltage coupling can arise from linearized ion channels (Sabah and Leibovic 1969; Mauro et al. 1970; Koch 1999; Richardson et al. 2003) or from the interaction with a passive dendritic compartment which is not modeled explicitly. Voltage coupling via the parameter a with a > 0 acts like a negative feedback and leads to adaptation. This parameter can arise from the linearization of “resonant currents” (Hutcheon and Yarom 2000) and can give rise to damped subthreshold oscillations. In the AdEx we assume that the voltage coupling (characterized by a) and the spike-triggered adaptation (characterized by b) have a similar time constant and can thus be lumped together in a single equation (Eq. 1). The biophysical mechanisms for spike-triggered adaptation are ion channels such as I_{M}, I_{K(slow)}, or I_{K(Ca)} (Benda and Herz 2003). Note that the spike triggered adaptation has contributions from both the a-term and the b-term. Having two parameters (a and b) allows to decouple the spike-triggered adaptation from the voltage coupling.
3 3 Multiple firing patterns
Type | C (pF) | g_{L} (nS) | E_{L} (mV) | V_{T} (mV) | ΔT (mV) | a (nS) | τ_{w} (ms) | b (pA) | V_{r} (mV) | C(β) | I (pA) |
---|---|---|---|---|---|---|---|---|---|---|---|
Fig. 8, cNA | 59 | 2.9 | −62 | −42 | 3.0 | 1.8 | 16 | 61 | −54 | 8.4 | 184 |
Fig. 8, cAD | 83 | 1.7 | −59 | −56 | 5.5 | 2.0 | 41 | 55 | −54 | 10.4 | 116 |
Fig. 8, RS | 104 | 4.3 | −65 | −52 | 0.8 | −0.8 | 88 | 65 | −53 | 10.4 | 98 |
Fig. 4aa | 200 | 10 | −70 | −50 | 2 | 2 | 30 | 0 | −58 | − | 500 |
Fig. 4b | 200 | 12 | −70 | −50 | 2 | 2 | 300 | 60 | −58 | − | 500 |
Fig. 4c | 130 | 18 | −58 | −50 | 2 | 4 | 150 | 120 | −50 | − | 400 |
Fig. 4d | 200 | 10 | −58 | −50 | 2 | 2 | 120 | 100 | −46 | − | 210 |
Fig. 4e | 200 | 12 | −70 | −50 | 2 | −10 | 300 | 0 | −58 | − | 300 |
Fig. 4f | 200 | 12 | −70 | −50 | 2 | −6 | 300 | 0 | −58 | − | 110 |
Fig. 4g | 100 | 10 | −65 | −50 | 2 | −10 | 90 | 30 | −47 | − | 350 |
100 | 12 | −60 | −50 | 2 | −11 | 130 | 30 | −48 | − | 160 |
Sharp versus broad spike after potential (SAP) The AdEx can produce adapting and tonic traces of two qualitatively different types. In Fig. 3a we see an example of sharp SAPs where the potential increases monotonically after a rapid downswing of the action potential. This type of reset is commonly seen in fast spiking interneurons (Bean 2007), and corresponds to a low value of the voltage reset V_{r} , combined with a weak spike-triggered adaptation b. In the phase plane we see that the reset is made at a point below the V-nullcline.
Broad SAPs, on the other hand, are observed in regular spiking pyramidal neurons (Connors and Gutnick 1990) and in the continuously adapting interneurons. A broad SAP is recognized by its low curvature at all times after the spike (Fig. 3b).
Initial bursting versus adapting Initial bursting denotes a group of spikes that were emitted at a frequency considerably greater than the steady-state frequency. This definition is very ambiguous and in many experimental traces initial bursting is indistinguishable from pronounced adaptation. In the framework of the AdEx, a clear definition becomes apparent. Initial bursting arises when the spiking starts with one or several sharp reset followed by broad resets only (Fig. 4c). With this definition, there is a qualitative difference between the SAP forming the initial burst and the SAP forming the tonic spiking that follows, as observed in experiments (Markram et al. 2004).
Regular bursting Regular bursting appears in a scenario similar to initial bursting except that the trajectory starting after the first broad reset projects below at least one of the previous reset points in the phase plane, such that the next reset point is below the V-nullcline; i.e. the next reset is sharp (Fig. 4d). This situation leads to an alternation between sharp and broad resets. Regular bursting is made possible with a V_{r} higher than the effective threshold V_{T} (note that V_{T} corresponds to the minimum of the V-nullcline), so as to ‘shield’ some reset points on the right hand side of the V-nullcline.
Delayed spiking and acceleration A negative a acts as a positive feedback which can be responsible for delayed initiation. Delayed spiking appears when injecting a current close to the rheobase. Because of a < 0, the (hyperpolarizing) adaptation current w is slowly decreasing at a depolarized V, allowing the neuron to eventually spike once the adaptation has decreased sufficiently (Fig. 4e). In the phase plane, the trajectory of the first spike stays below, but follows the contour of the V-nullcline. As it approaches the V-nullcline, the trajectory slows down since the magnitude of |dV / dt| decreases. With a < 0, the system of equations of the AdEx can lead to spike frequency acceleration (Fig. 4e, similar to experiments by Beierlein et al. (2003)) if the spike-triggered adaptation b is weak, as shown in Fig. 4e. For greater values of spike-triggered adaptation, it is also possible to get a delayed adapting trace (not shown) and a delayed bursting trace (Fig. 4f). Note that in both Fig. 4e and f, the trajectory starts with negative slope (dw /dt < 0 while dV / dt > 0) reflecting the decreasing adaptation current.
Rebound or transient spikes Post-inhibitory rebound is seen in several types of neurons. When a hyperpolarizing step current is released abruptly, some neurons will spike one or several time(s) before reaching their state of rest. This phenomenon is very similar to transient spiking observed during a step of depolarizing current. In both cases a stable fixed point remains, even after the sudden increase in current. A spike is nevertheless produced because the adaptation current is too slow to compensate for the sharp change in current. If the applied current was increased gradually to the same step size, the neuron would not spike because it would have had time to adapt. In the phase plane, rebound corresponds to a situation where the initial condition is located outside the basin of attraction of the stable fixed point, but the reset point is located inside this basin of attraction (Fig. 4g).
Irregular spiking Irregular spiking can occur in an AdEx model despite the fact that the equations are deterministic. Irregular spiking is manifest when the interspike interval keeps on changing without periodicity during the observation interval. There is an alternation of sharp and broad resets, but—unlike the case of initial bursting or regular bursting firing patterns—in the case of irregular spiking the sequence is not periodic, reminiscent of stuttering cells observed in some nest basket cells (Wang et al. 2002). The irregular firing pattern appears for a restricted set of parameters, and the volume occupied by the irregular spiking pattern in the parameter space seems to be patchy. We verified that the behavior was not due to a numerical artifact: Simulating the model neuron with the same parameter set with different numerical integration methods (reducing the forward-Euler time step, using Runge-Kutta or Adams-Bashforth-Moulton) always produced an irregular spiking pattern. Even though the exact spike train was not identical, spiking was aperiodic so that the classification of the spike pattern (adaptive, bursting, irregular) did not change after a change in the numerical method. Though other regions of the parameter space may produce irregular spiking, the pattern was found to be most noticeable at negative a, large spike-triggered adaptation (b) and high voltage reset (Fig. 4h).
4 4 Parameter space
Given the definitions for each firing pattern, we can now investigate how these firing patterns depend on the specific set of parameters. Since this problem depends only on the bifurcation parameters, we fixed the scaling parameters to realistic values: C = 100 pF, g_{L} = 10 nS, E_{L} = −70 mV, V_{T} = −50 mV and Δ_{T} = 2 mV. This corresponds to a membrane time constant τ = C/g_{L} of 10 ms. Resting potential (E_{L}), effective threshold (V_{T}) and slope factor (Δ_{T}) are in the range seen in experiments (Badel et al. 2007; Clopath et al. 2007). Changing the scaling parameters does not change the set of firing patterns accessible for a given set of bifurcation parameters, but may modify the amount of current necessary to go from one firing type to another. In addition, some firing patterns exist only close to the rheobase current (delayed spiking, transient spiking). In this section we will consider only the firing patterns that appear at a step current twice the amplitude of the rheobase current. The rheobase was determined according to analytical expressions given in Eqs. 5 and 6. The firing pattern is likely to change for different step current amplitudes, but we limit our study to just one amplitude of current step.
tonic: strictly sharp resets or strictly broad resets and −0.01 < A < 0.01;
adapting: strictly sharp resets or strictly broad resets and A > 0.01;
accelerating: strictly sharp resets or strictly broad resets and A < −0.01;
initial bursting: ordered sequence going from sharp to broad resets (e.g. sharp-sharp-broad-broad-broad);
regular bursting: alternation between broad and sharp SAP such that the number of sharp resets between each broad reset is constant;
irregular spiking: alternation between broad and sharp SAP such that the number of sharp resets between each broad reset is not constant after the third broad reset;
All programs were written in Matlab (The Mathworks, Natick, MA) and ran on a personal computer.
The adaptive models (τ_{w}= 100 ms, Fig. 6a, c, e) have bursting firing patterns extending to larger areas in the parameter space than the refractory models (τ_{w} = 5 ms, Fig. 6b, d, f), which have their diversity almost uniquely constrained to resets above V_{T}. In all cases, the border between bursting (initial or regular) and tonic-adapting firing patterns can be approximated by a curve with linear plus exponential terms, similar to a shifted version of the V-nullcline. This can be related to the qualitative arguments in Sect. 3 (see subsection on initial bursting above and Fig. 4e as well as the Sect. 5 below). In Fig. 6a and b, the tonic spiking (red areas) at low b and at high b corresponds to tonic with sharp resets and tonic with broad resets, respectively.
An adaptive current with high subthreshold adaptation yields predominantly bursting (initial and regular) and adapting firing patterns. However, a strong subthreshold adaptation is not sufficient on its own to model adapting and bursting patterns (Fig. 6d), but needs to be combined with high values of V_{r}. The refractory time constant in that case is often smaller than the interspike interval, this prevents cumulative increase of w since the adaptation current decays almost completely between spikes. Parameters combinations leading to irregular spiking (black pixels in Fig. 6) make a sparse and patchy structure that is entirely contained in the larger region of regular bursting. This firing pattern appears predominantly close to the border between initial and regular bursting.
Delayed spiking and transient spiking are absent from Fig. 6 because these firing patterns do not exist when the current is well above the rheobase. Delayed spiking will appear at injection current slightly above the rheobase and are more salient at low or negative a. Transient spiking is produced with current not sufficient to make the stable fixed point loose stability, and this firing pattern depends heavily on a as it occurs only for sizable a. The accelerating firing pattern is also associated with current close to rheobase. Nevertheless Fig. 6a shows that the accelerating firing pattern persists at higher step current amplitudes given that b is very small and V_{r} is within a small range below V_{T}.
5 5 Boundaries in parameter space
In Fig. 6c, the boundary between initial bursting and tonic spiking appears as a line that can be described as linear plus exponential terms. Hence the shape of the boundary reflects the shape of the V-nullcline. In this section we analyze why this is so.
More importantly for the present study, we can use the above result to get an approximation for the evolution of the adaptation variable w during spiking. After a spike, a trajectory restarts at the reset point (V_{r}, w_{r}) with a w-value w_{r} calculated from the reset condition in Eq. 3. Along the trajectory, both V and w change. A trajectory ends if V hits the upper bound V = 0 at which point the dynamics is reset and the next trajectory starts. We focus on a function M that maps the starting point (V_{r}, w_{r}) of the trajectory to the value w_{e} of the adaptation variable at the end of the trajectory. We note that this function depends on V_{r} and write w_{e} = M(w_{r}, V_{r}).
tonic spiking with broad resets if b > F(V_{r}) + I,
tonic spiking with sharp resets if b < F(V_{r}) − F(V_{T}) + X(V_{T}),
initial bursting if F(V_{r}) + I > b > F(V_{r}) − F(V_{T}) + X(V_{T}).
tonic spiking with broad resets if b > F(V_{r}}) + I,
regular bursting if b < F(V_{r}) − F(V_{T}) + X(V_{T}),
initial bursting if F(V_{r}) + I > b > F(V_{r}) − F(V_{T}) + X(V_{T}).
These relations can be used to draw a picture of the parameter space for the case of a piecewise linear V-nullcline F(V) (Fig. 7a) which holds in the limit of separation of timescales. Note that in the AdEx model of Fig. 6c, we have τ/τ_{w} = 0.1 so that the assumption of a separation of timescales is justifiable. Indeed, in the AdEx model the boundaries between the firing patterns in Fig. 6c reflects the shape of the nullclines as should be expected based on the results of this section.
6 6 Comparison with cortical neurons
We can test that the AdEx model accurately reproduces the firing patterns of real neurons by comparing with experimental injections of step currents into neurons of the cortex (data, courtesy of Henry Markram and Maria Toledo-Rodriguez, see Markram et al. (2004) and Toledo-Rodriguez et al. (2004) for the complete details on the experiments). Briefly, the experiments consist of 2–5 repetitions of 2 s step current injections with three different amplitudes. The amplitude of the steps ranged from 100 to 200 pA. The electrophysiological class was defined according to Markram et al. (2004) for the inhibitory neurons and according to Connors and Gutnick (1990) for the excitatory neurons. We will be considering only three different classes. In particular, we will compare the AdEx with two types of inhibitory neurons (continuous accommodating, cAD, and continuous non-adapting, cNA) and one type of excitatory neuron (regular spiking, RS).
In order to compare the experiments with the AdEx model, we fit the parameters of the model neuron per neuron. The fitting method was chosen for the ease of implementation and for the capability to handle an optimization problem with many local minima (see further below). Optimized model traces are compared with the experimental traces in Fig. 8. We can see that the AdEx model offers a good qualitative match akin to optimized Hodgkin and Huxley models (Druckmann et al. 2007). The optimized parameters for each chosen cell are given in Table 1. From this table, we see that the inhibitory cells correspond to smaller membrane capacitance, consistent with the smaller size of these cells. The parameter a is low for all three cells and does not strongly influence the features used as optimization criteria. The timescale of the adaptation is the largest for the RS cells, which is expected because of the slow adaptation currents known to be present in these cells. Figure 8d–f show the steady-state f-I curve for the fitted models corresponding to the three different cells. We see that the two interneuron models show type II f-I curves of a high slope, while the RS neuron have an f-I curve of I slow slope. Although the full f-I curve was not available with the experiments we used, we can see a qualitative link with the stereotypical f-I curves that are to be expected for these types of cells (Tateno et al. 2004).
f_{1}: Number of spikes, n,
f_{2}: First spike latency,
f_{3}: First inter-spike interval,
f_{4}: Last inter-spike interval,
f_{5}: First inter-spike minimum potential,
f_{6}: Waveform before the first spike.
This is minimized with a genetic algorithm with a population of 100 individuals for 200 generations and a crossover fraction of 0.6.
7 7 Discussion
The AdEx model can produce multiple firing patterns depending on parameters. The model neuron can exhibit initial bursting, regularly bursting, tonic spiking, adapting, accelerating, irregular spiking, or show delayed initiation. We have drawn clear definitions of these firing patterns in terms of two types of spiking trajectories. The two types of spiking trajectories depends on whether the adaptation current immediately after spiking is strong enough to make the membrane potential decrease slowly before starting to increase in preparation for the next spike. The explicit distinction between continuous adaptation, initial bursting, and the description of irregular spiking extends previous work on firing patterns in model neurons (Izhikevich 2003, 2007; Touboul 2008). Whereas Touboul and Brette (2008) focuses on types of excitability, stationary limit cycles and their relation to the underlying bifurcations, we studied the transient response to step currents since this is the prevalent experimental paradigm for classifying firing patterns.
Chaotic responses have been observed in neuron models as well as real neurons under periodic forcing (Holden 1986; Brumberg and Gutkin 2007). The type of chaos that we report does not depend on a periodic forcing. Rather it is closely related to the chaos observed in Hodgkin-Huxley equations (Chay and Rinzel 1985). The presence of chaos in the AdEx model is restricted to small regions of the parameter space (Fig. 6). It is nevertheless possible that neurons access a similar chaotic regime. Evidences for the existence of chaos at all levels of the nervous system accumulate, despite technical limitations (Korn and Faure 2003). Figure 5 provides a method with which experiments could be made to verify the existence of the irregular firing pattern described. The irregular firing pattern appears in a narrow range of step current amplitudes (Fig. 5a) surrounded by a bursting at smaller and stronger current amplitude. Irregularity can be distinguished from noise by looking at the interval map, which is a thin continuous line (Fig. 5b) for the irregular spiking AdEx model.
The AdEx model can be fit with good agreement to three types of cortical neurons, as can be seen from the comparison between model and experimental traces on the time scale of seconds (two first columns in Fig. 8). A closer look on the overlay unveils some discrepancies: the spike initiation of the cAD and FS interneurons is not fully captured by the model. The first interspike interval is too long in the case for the RS cell, while the last interspike interval is too short in the cNA cell. This makes the trade-off between different features evident, and our simple model cannot fit with high precision both the initial burst of RS cells and the broad SAP observed in these cells. Important improvement is to be expected by adding another adaptation current but here we restricted the investigation to the capability of only one adaptation variable.
Our study has shown that the value of the voltage reset is an important parameter of the model. The voltage reset allows to include some, but not all refractory properties in a neuron model (Badel et al. 2007). Refractoriness arises from the combined contributions of increased firing threshold, reduced membrane time constant and hyperpolarized voltage. If the point-neuron model is replaced by a multi-compartment model, the effective voltage reset depends also on the amount of current flowing from the dendritic compartments into the soma. This can lead to an After Spike Depolarization (ADP). The most convenient choice for a voltage reset would be the maximum of the ADP. Mainen and Sejnowski (1996) have shown previously that variable electrical coupling with an active dendritic compartment tunes the ADP and can be made responsible for multiple firing patterns. The AdEx provides a simpler framework to switch between firing patterns by changing reset parameters—and these changes could indeed reflect, amongst other influences, changes in electrical coupling.
We can conclude that the diversity of firing patterns is explained in the AdEX model by simple dynamical processes that can be completely analyzed in the phase plane. The AdEx model represents an attractive candidate neuron model for use in large-scale network simulations. Earlier studies have shown that it is sufficiently accurate for the prediction of spike timing when a RS pyramidal neuron receives noisy current injection at the soma (Jolivet et al. 2007). Network simulations of large systems such as a column of the barrel cortex (Markram 2006), or thalamocortical systems (Hill and Tononi 2005; Izhikevich and Edelman 2008) require the description of different neuron types. In this paper we addressed this issue by providing parameter sets that describe three types of cortical neurons, within the framework of a single and attractively simple neuron model. Further work will be needed to extend this to a larger number of neuron types and stimulation paradigms.
^{1}
Footnotes
- 1.
Also referred to as aEIF.
Notes
Acknowledgments
Many thanks to Henry Markram, Felix Schürmann and Maria Toledo-Rodriguez for providing the experimental traces and for helpful discussions. R. Naud is supported by the Fonds Québécois de la Recherche sur la Nature et les Technologie (FQRNT). This project is partly supported by funding of the European Union under the grant no. 15879 (FACETS)
Open Access
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