Dynamics of Individual Cells: Key Concepts

Linear Systems

Let stimuli I1 and I2 be any stimuli. Let u1 and u2 be the respective responses in some system. And lastly, let c be any constant. A system is linear if the response u elicted by the stimuli I1+I2 is

u = u1+u2

and the response due to the stimulus cI1 (c times the stimulus I1) is

u = c u1

We will focus on the former ("additive") property.

The following MPEG file illustrates the dynamics of a capacitor-inductor-resistor circuit whose power supply generates a current I(t), t=time. The measured response is the voltage u(t). The top panel are the voltages; the bottom panel are the stimuli (color coded to associated responses). Note: u = u1+u2

Nonlinear Systems

Any system which is not linear.

The following MPEG file illustrates nonlinearity in a model of cell electrical activity. Here u(t) is the transmembrane electrical potential at time t and the stimulus I(t) is an applied current. Note: it is not true that u = u1+u2

(Fitz-Hugh Nagumo model).

Excitable Systems and Excitability

Excitability is not a mathematical term. Roughly speaking, a system is excitable if it exhibits a threshold phenomenon with respect to a stimulus ( example ). Nerve cells are classic examples of excitable systems though there are chemical ones as well. If a nerve is stimulated with a small brief stimulus (current) the response (transmembrane potential) is small and resets to rest quickly. If the brief stimuli is of a sufficient magnitude (superthreshold) a large response (called an action potential) is ellicted. Typically in excitable systems, even larger super threshold responses do not ellict an even larger response.

The following MPEG file illustrates excitability of the barnacle muscle fiber's transmembrane potential u(t) in response to an applied current I(t).

(Morris-Lecar model)


Two common qualitative behaviors in a measured reponse are stable equilibria (or fixed points) and oscillations (or periodic solutions).

If for any initial value (condition) u(0) the response u(t) approaches a constant value U as time increases then U is said to be a (globally) stable equilibrium. Similarly , if u(t) approaches a periodic oscillation (orbit) U(t) then U(t) is said to be a (globally) stable periodic orbit of the system.

The following MPEG illustrates stable equilibria and periodic solutions in an activator-inhibitor system with two chemical species. The concentrations are u(t) and v(t). The stimulus is I(t)=0. The top panel shows an approach of u(t) to a stable periodic solution for different initial concentrations. The bottom panel shows an approach to a stable equilibria.

(Model from "Mathematical Biology", J. Murray).

Notice that in the periodic case, the resulting periodic solutions are out of phase but otherwise identical. To clearly illustrate this, it is often useful to graph the concentrations against each other. Here there are two chemicals of concentration u(t) and v(t) at time t, respectivley. The following MPEG file gives a plot of u(t) versus v(t) in the periodic case. The periodic "orbit" or "attractor" is clearly evident in the first of these movies. Here is an analogous movie for the equilibria case: MPEG


Some systems have more than one stable object (attractor) and depending on the initial state (or initial conditions) may relax to one or the other stable configuration. If the system has two different stable configurations it is said to be bistable. Bistability can be between equilibria and equilibria or between equilibria and periodic solutions. Also, bistability is only defined with regard to an absence of stimulus, i.e I=0.

The following MPEG file shows voltage u(t) approaching (locally) stable equilibria and periodic solutions. The model mimics the voltage in a pancreatic beta cell where the intracellular (free) Ca2+ concentration is held fixed. Here the stimulus I=0

If the same system is stimulated with an applied transmembrane current, current pulses can knock the system from one stable state to the other. The following MPEG file illustrates this. Note the first pulse is not of sufficient magnitude and/or duration to affect a jump to the stable periodic configuration. This reflects the excitability of the system.

(Burster Fast subsystem, Pernarowski 1994)

Remark: Bistability cannot occur in time-independent (autonomous) linear systems. Clearly, responses u(t) can vary in time so "time independence" means the "black box" in your system is not time dependent.


In the preceding example, the oscillation of the voltage would have continued were there no hyperpolarizing (last downward) stimulus. In some systems, a stimulus will cause the system to move from one stable configuration to what appears to be another stable configuration but that after long enough time the system returns to its initial state (or close to it). This is the idea behind adaptation.

Adaptation is difficult to define precisely. It is not a mathematical term (like excitability). Some people define adaptation as the return to an initial state when the stimulus is stepped up to a constant value.

The following MPEG file illustrates adaptation in the membrane potential u(t) given a stimulus (current) which is initially zero and then stepped up to a constant value. The constant stimulus initiates oscillations whose frequency decreases slowly and then disappear altogether. After some time, the system returns to a rest state (equilibrium) near the initial rest state.

The model mimics the membrane potential of an individual pancreatic beta cell at subthreshold glucose levels with an applied current stimulus.

Endogenous Oscillations

If a system is bistable a slow change in the stimulus can cause transitions between stable equilibria and periodic solutions. A slowly changing regulatory chemical or process can do pretty much the same thing. If this is the case then complicated "endogenous" oscillations are possible even when the stimulus I=0.

In all of the following MPEG files membrane potential u(t) and concentration c(t) of a slow regulatory chemical are graphed. In all examples, u(t) is a "fast" variable and c(t) is a "slow" variable. Fastness and slowness is a relative concept. In the examples, u(t) changes alot in a short time (spikes) even though the concentration c(t) does not change appreciably. Thus it is more the relative time rates of change of u(t) or c(t) which make them fast or slow.

Bursting This oscillation is observed in many mouse pancreatic beta cells at intermediate glucose concentrations. This particular "bursting" pattern is called square wave bursting for obvious reasons. Notice c(t) does not change much during a spike - hence is slow.

Beat In some experiments, beta cells don't burst. Instead they spike irradically - not periodically as illustrated in this example. What is interesting here is that the small amplitude oscillations are essential for maintaining this pattern. If c(t) were (experimentally) maintained at its average value these oscillations might not occur!

"Nearly" parabolic bursting Shows patterns similar to those observed in R15 snail neurons. Note here that the transitions between spiking and nonspiking does not seem to depend on the value of u(t) as in square wave bursting above! This is a "qualitative" feature.

Tapered bursting A different kind of electrical oscillation (cat pyramidal neurons?).

Excitation/Inhibition and Refractoriness

As previously mentioned, the concept of "excitability" is not a precise mathematical term but is nevertheless a useful concept for describing the response of certain systems. Two additional and related concepts used in electrophysiology are "Inhibition" and "Refractoriness". The following MPEG file illustrates these concepts by way of example. The top figure shows the electrical potential u(t) of a barnacle muscle fiber as a function of time t. The bottom figure shows the current stimulus which caused that response. The model is the same "Morris-Lecar" model used to illustrate the concept of excitability above.

The first current pulse at t=100 is "excitatory" since it ellicts an excited pulse (action potential/spike).

Near t=400 the cell receives two pulse inputs in succession. The second of these pulses is identical to the excitatory input at t=100 yet the response is different. This is because the first pulse at t=380 is "inhibitory". The degree of inhibition has to due with the duration and magnitude of the inhibitory pulse as well as the time between the inhibitory and excitatory pulses. Also, since the system is nonlinear, the response is no a sum of the responses elicited by inhibitory and excitatory pulses if they were applied separately.

Lastly, the group of two (positive) pulses near t=700 elicit a sole action potential and not two pulses. Neurons exhibit a "period of refractoriness" where, once a spike has been initiated, no similar excitatory pulse will cause a second (significant) excitatory response. If the second pulse were delayed long enough, a second spike would have been observed. In experiments where many spikes are measured there usually is a minimum "interspike interval" (ISI) due to the cell's refractoriness.