Comparison of Coding Capabilities of Type I and Type II Neurons

We consider the dependence of information transfer by neurons on the Type I vs. Type II classification of their dynamics. Our computational study is based on Type I and II implementations of the Morris-Lecar model. It mainly concerns neurons, such as those in the auditory or electrosensory system, which encode band-limited amplitude modulations of a periodic carrier signal, and which fire at random cycles yet preferred phases of this carrier. We first show that the Morris-Lecar model with additive broadband noise ("synaptic noise") can exhibit such firing patterns with either Type I or II dynamics, with or without amplitude modulations of the carrier. We then compare the encoding of band-limited random amplitude modulations for both dynamical types. The comparison relies on a parameter calibration that closely matches firing rates for both models across a range of parameters. In the absence of synaptic noise, Type I performs slightly better than Type II, and its performance is optimal for perithreshold signals. However, Type II performs well over a slightly larger range of inputs, and this range lies mostly in the subthreshold region. Further, Type II performs marginally better than Type I when synaptic noise, which yields more realistic baseline firing patterns, is present in both models. These results are discussed in terms of the tuning and phase locking properties of the models with deterministic and stochastic inputs.     

The amplitude sensitivity of mouse inferior collicular neurons in the presence of weak noise

Natural auditory environment consists of multiple sound sources that are embedded in ambient strong and weak noise. For effective sound communication and signal analysis, animals must somehow extract biologically relevant signals from the inevitable interference of ambient noise. The present study examined how a weak noise may affect the amplitude sensitivity of neurons in the mouse central nucleus of the inferior colliculus (IC) which receives convergent excitatory and inhibitory inputs from both lower and higher auditory centers. Specifically, we studied the amplitude sensitivity of IC neurons using a probe (best frequency pulse) and a masker (weak noise) under simultaneous masking paradigm. For most IC neurons, weak noise masking increases the minimum threshold and decreases the number of impulses. Noise masking also increased the slope and decreased the dynamic range of the rate amplitude function of these IC neurons. The strength of this noise masking was greater at low than at high sound amplitudes. This variation in the amplitude sensitivity of IC neurons in the presence of the weak noise was mostly mediated through GABAergic inhibition. These data indicate that in the real world the ambient weak noise improves amplitude sensitivity of IC neurons through GABAergic inhibition while inevitably decreases the range of overall auditory sensitivity of IC neurons.     

Synaptic limitations to contrast coding in the retina of the blowfly Calliphora

We investigate the effects of synaptic transmission on early visual processing by examining the passage of signals from photoreceptors to second order neurons (LMCS). We concentrate on the roles played by three properties of synaptic transmission: (1) the shape of the characteristic curve, relating pre- and postsynaptic signal amplitudes, (2) the dynamics of synaptic transmission and (3) the noise introduced during transmission. The characteristic curve is sigmoidal and follows a simple model of synaptic transmission (Appendix) in which transmitter release rises exponentially with presynaptic potential. According to this model a presynaptic depolarization of 1.50-1.86 mV produces an e-fold increase in postsynaptic conductance. The characteristic curve generates a sigmoidal relation between postsynaptic (LMC) response amplitude and stimulus contrast. The shape and slope of the characteristic curve is unaffected by the state of light adaptation. Retinal antagonism adjusts the characteristic curve to keep it centred on the mean level of receptor response generated by the background. Thus the photoreceptor synapses operate in the mid-region of the curve, where the slope or gain is highest and equals approximately 6. The dynamics of transmission of a signal from photoreceptor to second-order neuron approximates to the sum of two processes with exponential time courses. A momentary receptor depolarization generates a postsynaptic hyperpolarization of time constant 0.5-1.0 ms, followed by a slower and weaker depolarization. Light adaptation increases the relative amplitude of the depolarizing process and reduces its time constant from 80 ms to 1.5 ms. The hyperpolarizing process is too rapid to bandlimit receptor signals. The noise introduced during the passage of the signal from receptor to second-order neuron is measured by comparing signal:noise ratios and noise power spectra in the two cell types. Under daylight conditions from 50 to 70% of the total noise power is generated by events associated with the transmission of photoreceptor signals and the generation of LMC responses. According to the exponential model of transmitter release, the effects of synaptic noise are minimized when synaptic gain is maximized. Moreover, both retinal antagonism and the sigmoidal shape of the characteristic curve promote synaptic gain. We conclude that retinal antagonism and nonlinear synaptic amplification act in concert to protect receptor signals from contamination by synaptic noise. This action may explain the widespread occurrence of these processes in early visual processing.     

Effects of noise bandwidth and amplitude modulation on masking in frog auditory midbrain neurons

Natural auditory scenes such as frog choruses consist of multiple sound sources (i.e., individual vocalizing males) producing sounds that overlap extensively in time and spectrum, often in the presence of other biotic and abiotic background noise. Detection of a signal in such environments is challenging, but it is facilitated when the noise shares common amplitude modulations across a wide frequency range, due to a phenomenon called comodulation masking release (CMR). Here, we examined how properties of the background noise, such as its bandwidth and amplitude modulation, influence the detection threshold of a target sound (pulsed amplitude modulated tones) by single neurons in the frog auditory midbrain. We found that for both modulated and unmodulated masking noise, masking was generally stronger with increasing bandwidth, but it was weakened for the widest bandwidths. Masking was less for modulated noise than for unmodulated noise for all bandwidths. However, responses were heterogeneous, and only for a subpopulation of neurons the detection of the probe was facilitated when the bandwidth of the modulated masker was increased beyond a certain bandwidth - such neurons might contribute to CMR. We observed evidence that suggests that the dips in the noise amplitude are exploited by TS neurons, and observed strong responses to target signals occurring during such dips. However, the interactions between the probe and masker responses were nonlinear, and other mechanisms, e.g., selective suppression of the response to the noise, may also be involved in the masking release.     

Model of brain rhythmic activity

1. A model of a neuronal network has been set up in a digital computer based on histological and biophysical data experimentally obtained from the thalamus; the model includes two populations of neurons interconnected by means of negative feedback; in the model allowance is also made for other sort of interactions.
2. To test the hypothesis that the alpha-rhythm (8–13 Hz rhythmic activity characteristic of the EEG) is a filtered noise signal the simulated neuronal network was stimulated by random trains of pulses with a Poisson distribution. The density of pulses fired by the simulated neurons was computed as well as the oscillations of the mean membrane potential of the population of simulated neurons. The latter was found to be equivalent to the experimentally obtained alpha rhythms.
3. In order to test the hypothesis that several noise sources are responsible for thalamo-cortical coherences three simulated neuronal networks were coupled together using several noise sources as secondary inputs. It was shown that although all the networks produced simulated alpha signals with identical spectra they could have significantly different values of coherence depending on the relation between correlated and uncorrelated input signals.
4. The model was analysed by means of linear systems analysis after introducing the necessary simplifications and approximations. In this way it was possible to evaluate the influence of different physiological or histological parameters upon the statistical properties of the resulting rhythmic activity in an analytical form.
5. By changing the model parameters it was shown that a family of spectral curves could be obtained which simulated the development of the EEG as function of age from a predominantly low frequency to a clearly rhythmic type of signal. This was shown to depend mainly on the feedback coupling parameters.

     

Neural dynamics of envelope coding

We consider the processing of narrowband signals that modulate carrier waveforms in sensory systems. The tuning of sensory neurons to the carrier frequency results in a high sensitivity to the amplitude modulations of the carrier. Recent work has revealed how specialized circuitry can extract the lower-frequency modulation associated with the slow envelope of a narrowband signal, and send it to higher brain along with the full signal. This paper first summarizes the experimental evidence for this processing in the context of electroreception, where the narrowband signals arise in the context of social communication between the animals. It then examines the mechanism of this extraction by single neurons and neural populations, using intracellular recordings and new modeling results contrasting envelope extraction and stochastic resonance. Low noise and peri-threshold stimulation are necessary to obtain a firing pattern that shows high coherence with the envelope of the input. Further, the output must be fed through a slow synapse. Averaging networks are then considered for their ability to detect, using additional noise, signals with power in the envelope bandwidth. The circuitry that does support envelope extraction beyond the primary receptors is available in many areas of the brain including cortex. The mechanism of envelope extraction and its gating by noise and bias currents is thus accessible to non-carrier-based coding as well, as long as the input to the circuit is a narrowband signal. Novel results are also presented on a more biophysical model of the receptor population, showing that it can encode a narrowband signal, but not its envelope, as observed experimentally. The model is modified from previous models by stimulus reducing contrast in order to make it sufficiently linear to agree with the experimental data.     

Noise-induced neural impulses

The firing pattern of neural pulses often show the following features: the shapes of individual pulses are nearly identical and frequency independent; the firing frequency can vary over a broad range; the time period between pulses shows a stochastic scatter. This behaviour cannot be understood on the basis of a deterministic non-linear dynamic process, e.g. the Bonhoeffer-van der Pol model. We demonstrate in this paper that a noise term added to the Bonhoeffer-van der Pol model can reproduce the firing patterns of neurons very well. For this purpose we have considered the Fokker-Planck equation corresponding to the stochastic Bonhoeffer-van der Pol model. This equation has been solved by a new Monte Carlo algorithm. We demonstrate that the ensuing distribution functions represent only the global characteristics of the underlying force field: lines of zero slope which attract nearby trajectories prove to be the regions of phase space where the distributions concentrate their amplitude. Since there are two such lines the distributions are bimodal representing repeated fluctuations between two lines of zero slope. Even in cases where the deterministic Bonhoeffer-van der Pol model does not show limit cycle behaviour the stochastic system produces a limit cycle. This cycle can be identified with the firing of neural pulses.     

Mechanisms That Enhance Sustainability of p53 Pulses

The tumor suppressor p53 protein shows various dynamic responses depending on the types and extent of cellular stresses. In particular, in response to DNA damage induced by γ-irradiation, cells generate a series of p53 pulses. Recent research has shown the importance of sustaining repeated p53 pulses for recovery from DNA damage. However, far too little attention has been paid to understanding how cells can sustain p53 pulses given the complexities of genetic heterogeneity and intrinsic noise. Here, we explore potential molecular mechanisms that enhance the sustainability of p53 pulses by developing a new mathematical model of the p53 regulatory system. This model can reproduce many experimental results that describe the dynamics of p53 pulses. By simulating the model both deterministically and stochastically, we found three potential mechanisms that improve the sustainability of p53 pulses: 1) the recently identified positive feedback loop between p53 and Rorα allows cells to sustain p53 pulses with high amplitude over a wide range of conditions, 2) intrinsic noise can often prevent the dampening of p53 pulses even after mutations, and 3) coupling of p53 pulses in neighboring cells via cytochrome-c significantly reduces the chance of failure in sustaining p53 pulses in the presence of heterogeneity among cells. Finally, in light of these results, we propose testable experiments that can reveal important mechanisms underlying p53 dynamics.     

Analysis of neuronal networks in the visual system of the cat using statistical signals — Simple and complex cells

Superimposing additively a two-dimensional noise process to deterministic input signals (bars) the neurons of area 17 show a class-specific reaction for the task of signal extraction. Moving both parts of the signals simultaneously and varying the signal to noise ratio (S/N) the simple cells achieve the same performance as resulted from the psychophysical experiment. Type I complex cells extract moving deterministic signals (i.e. bars) from the stationary noise, whereas in the answers of Type II complex cells the statistical parts of the signals predominate. Considering the different cell types each as a series of a linear and a nonlinear system one obtains the cell specific space-time frequency and the amplitude characteristics.This work was supported by DFG Grant Ho 450/6 and Grant Se 251/9     

Membrane Resonance and Stochastic Resonance Modulate Firing Patterns of Thalamocortical Neurons

We examined the interactions of subthreshold membrane resonance and stochastic resonance using whole-cell patch clamp recordings in thalamocortical neurons of rat brain slices, as well as with a Hodgkin-Huxley-type mathematical model of thalamocortical neurons. The neurons exhibited the subthreshold resonance when stimulated with small amplitude sine wave currents of varying frequency, and stochastic resonance when noise was added to sine wave inputs. Stochastic resonance was manifest as a maximum in signal-to-noise ratio of output response to subthreshold periodic input combined with noise. Stochastic resonance in conjunction with subthreshold resonance resulted in action potential patterns that showed frequency selectivity for periodic inputs. Stochastic resonance was maximal near subthreshold resonance frequency and a high noise level was required for detection of high frequency signals. We speculate that combined membrane and stochastic resonances have physiological utility in coupling synaptic activity to preferred firing frequency and in network synchronization under noise.     

A common goodness-of-fit framework for neural population models using marked point process time-rescaling

The noisy threshold regime, where even a small set of presynaptic neurons can significantly affect postsynaptic spike-timing, is suggested as a key requisite for computation in neurons with high variability. It also has been proposed that signals under the noisy conditions are successfully transferred by a few strong synapses and/or by an assembly of nearly synchronous synaptic activities. We analytically investigate the impact of a transient signaling input on a leaky integrate-and-fire postsynaptic neuron that receives background noise near the threshold regime. The signaling input models a single strong synapse or a set of synchronous synapses, while the background noise represents a lot of weak synapses. We find an analytic solution that explains how the first-passage time (ISI) density is changed by transient signaling input. The analysis allows us to connect properties of the signaling input like spike timing and amplitude with postsynaptic first-passage time density in a noisy environment. Based on the analytic solution, we calculate the Fisher information with respect to the signaling input’s amplitude. For a wide range of amplitudes, we observe a non-monotonic behavior for the Fisher information as a function of background noise. Moreover, Fisher information non-trivially depends on the signaling input’s amplitude; changing the amplitude, we observe one maximum in the high level of the background noise. The single maximum splits into two maximums in the low noise regime. This finding demonstrates the benefit of the analytic solution in investigating signal transfer by neurons.     

Odorant receptors activated by amino acids in sensory neurons of the channel catfish Ictalurus punctatus

Odorant receptors activated by amino acids were investigated with patch- clamp techniques in olfactory receptor neurons of the channel catfish, Ictalurus punctatus. The L-isomers of alanine, norvaline, arginine, and glutamate, known to act predominantly on different olfactory receptor sites, activated nondesensitizing inward currents with amplitudes of - 2.5 to -280 pA in olfactory neurons voltage-clamped at membrane potentials of -72 or -82 mV. Different amino acids were shown to induce responses in the same sensory neurons; however, the amplitude and the kinetics of the observed whole cell currents differed among the stimuli and may therefore reflect activation of different amino acid receptor types or combinations of receptor types in these cells. Amino acid- induced currents appeared to have diverse voltage dependence and could also be classified according to the amplitude of the spontaneous channel fluctuations underlying the macroscopic currents. A mean single- channel conductance (gamma) of 360 fS was estimated from small noise whole-cell currents evoked by arginine within the same olfactory neuron in which a mean gamma value of 23.6 pS was estimated from ''large noise'' response to norvaline. Quiescent olfactory neurons fired bursts of action potentials in response to either amino acid stimulation or application of 8-Br-cyclic GMP (100 microM), and voltage-gated channels underlying generation of action potentials were similar in these neurons. However, in whole-cell voltage-clamp, 8-Br-cyclic GMP evoked large rectangular current pulses, and single-channel conductances of 275, 220, and 110 pS were obtained from the discrete current levels. These results suggest that in addition to the cyclic nucleotide-gated transduction channels, olfactory neurons of the channel catfish possess a variety of odor receptors coupled to different types of transduction channels.     

A general odorant background affects the coding of pheromone stimulus intermittency in specialist olfactory receptor neurones

In nature the aerial trace of pheromone used by male moths to find a female appears as a train of discontinuous pulses separated by gaps among a complex odorant background constituted of plant volatiles. We investigated the effect of such background odor on behavior and coding of temporal parameters of pheromone pulse trains in the pheromone olfactory receptor neurons of Spodoptera littoralis. Effects of linalool background were tested by measuring walking behavior towards a source of pheromone. While velocity and orientation index did drop when linalool was turned on, both parameters recovered back to pre-background values after 40 s with linalool still present. Photo-ionization detector was used to characterize pulse delivery by our stimulator. The photo-ionization detector signal reached 71% of maximum amplitude at 50 ms pulses and followed the stimulus period at repetition rates up to 10 pulses/s. However, at high pulse rates the concentration of the odorant did not return to base level during inter-pulse intervals. Linalool decreased the intensity and shortened the response of receptor neurons to pulses. High contrast (>10 dB) in firing rate between pulses and inter-pulse intervals was observed for 1 and 4 pulses/s, both with and without background. Significantly more neurons followed the 4 pulses/s pattern when delivered over linalool; at the same time the information content was preserved almost to the control values. Rapid recovery of behavior shows that change of perceived intensity is more important than absolute stimulus intensity. While decreasing the response intensity, background odor preserved the temporal parameters of the specific signal.     

Neurons in the inferior colliculus of the big brown bat show maximal amplitude sensitivity at the best duration

The big brown bats, Eptesicus fuscus, emit ultrasonic signals and analyze the returning echoes in multi-parametric domains to extract target features. The variation of different pulse parameters during hunting predicts that analysis of an echo parameter by bats is inevitably affected by other co-varying echo parameters. In this study, we presented data to show that the bat inferior collicular (IC) neurons have maximal amplitude sensitivity at the best duration (BD). A family of rate-amplitude function (RAF) of each IC neuron is plotted with the BD and non-BD sound pulses. The RAF plotted with BD pulses has sharper slope (SL) and smaller dynamic range (DR) than the RAF plotted with non-BD pulses has. All RAFs can be described as monotonic, saturated or non-monotonic. IC neurons with monotonic RAF are mostly recorded at deeper IC and they have the largest average BD, best amplitude (BA) and DR. Conversely, IC neurons with non-monotonic RAF are mostly recorded at upper IC and they have the smallest average BD, BA and DR. Low best frequency (BF) neurons at upper IC have shorter BD, smaller BA and DR than high BF neurons at deeper IC have. These data suggest that IC neurons that tune to an echo duration also have the greatest sensitivity to echo amplitude. These data also suggest that sensitivity in frequency, duration and amplitude appears to be orderly represented along the dorso-ventral axis of the IC.     

Predicting the Responses of Repetitively Firing Neurons to Current Noise

We used phase resetting methods to predict firing patterns of rat subthalamic nucleus (STN) neurons when their rhythmic firing was densely perturbed by noise. We applied sequences of contiguous brief (0.5–2 ms) current pulses with amplitudes drawn from a Gaussian distribution (10–100 pA standard deviation) to autonomously firing STN neurons in slices. Current noise sequences increased the variability of spike times with little or no effect on the average firing rate. We measured the infinitesimal phase resetting curve (PRC) for each neuron using a noise-based method. A phase model consisting of only a firing rate and PRC was very accurate at predicting spike timing, accounting for more than 80% of spike time variance and reliably reproducing the spike-to-spike pattern of irregular firing. An approximation for the evolution of phase was used to predict the effect of firing rate and noise parameters on spike timing variability. It quantitatively predicted changes in variability of interspike intervals with variation in noise amplitude, pulse duration and firing rate over the normal range of STN spontaneous rates. When constant current was used to drive the cells to higher rates, the PRC was altered in size and shape and accurate predictions of the effects of noise relied on incorporating these changes into the prediction. Application of rate-neutral changes in conductance showed that changes in PRC shape arise from conductance changes known to accompany rate increases in STN neurons, rather than the rate increases themselves. Our results show that firing patterns of densely perturbed oscillators cannot readily be distinguished from those of neurons randomly excited to fire from the rest state. The spike timing of repetitively firing neurons may be quantitatively predicted from the input and their PRCs, even when they are so densely perturbed that they no longer fire rhythmically.     

Population Decoding in Rat Barrel Cortex: Optimizing the Linear Readout of Correlated Population Responses

Sensory information is encoded in the response of neuronal populations. How might this information be decoded by downstream neurons? Here we analyzed the responses of simultaneously recorded barrel cortex neurons to sinusoidal vibrations of varying amplitudes preceded by three adapting stimuli of 0, 6 and 12 µm in amplitude. Using the framework of signal detection theory, we quantified the performance of a linear decoder which sums the responses of neurons after applying an optimum set of weights. Optimum weights were found by the analytical solution that maximized the average signal-to-noise ratio based on Fisher linear discriminant analysis. This provided a biologically plausible decoder that took into account the neuronal variability, covariability, and signal correlations. The optimal decoder achieved consistent improvement in discrimination performance over simple pooling. Decorrelating neuronal responses by trial shuffling revealed that, unlike pooling, the performance of the optimal decoder was minimally affected by noise correlation. In the non-adapted state, noise correlation enhanced the performance of the optimal decoder for some populations. Under adaptation, however, noise correlation always degraded the performance of the optimal decoder. Nonetheless, sensory adaptation improved the performance of the optimal decoder mainly by increasing signal correlation more than noise correlation. Adaptation induced little systematic change in the relative direction of signal and noise. Thus, a decoder which was optimized under the non-adapted state generalized well across states of adaptation.     

Extracellular transduction events under pulsed stimulation in moth olfactory sensilla

In natural conditions, pheromones released continuously by female moths are broken in discontinuous clumps and filaments. These discontinuities are perceived by flying male moths as periodic variations in the concentration of the stimulus, which have been shown to be essential for location of females. We study analytically and numerically the evolution in time of the activated pheromone-receptor (signaling) complex in response to periodic pulses of pheromone. The 13-reaction model considered takes into account the transport of pheromone molecules by pheromone binding proteins (PBP), their enzymatic deactivation in the perireceptor space and their interaction with receptors at the dendritic membrane of neurons in Antheraea polyphemus sensitive to the main pheromone component. The time-averaged and periodic properties of the temporal evolution of the signaling complex are presented, in both transient and steady states. The same time-averaged response is shown to result from many different pulse trains and to depend hyperbolically on the time-averaged pheromone concentration in air. The dependency of the amplitude of the oscillations of the signaling complex on pulse characteristics, especially frequency, suggests that the model can account for the ability of the studied type of neuron to resolve repetitive pulses up to 2 Hz, as experimentally observed. Modifications of the model for resolving pulses up to 10 Hz, as found in other neuron types sensitive to the minor pheromone components, are discussed.     

Interspike interval statistics in the Ornstein-Uhlenbeck neuronal model with signal-dependent noise

The stochastic leaky integrate-and-fire (LIF) continuous model is studied under the condition that the amplitude of noise is a function of the input signal. The coefficient of variation (CV) of interspike intervals (ISIs) is investigated for different types of dependencies between the noise and the signal. Finally, we present the CV and the ISI density resulting from the special choice of parameters of the input that gave rise to a contra-intuitive behavior of the transfer function in Lánsky and Sacerdote [Phys. Lett. A 285 (2001) 132].     

Slope-Based Stochastic Resonance: How Noise Enables Phasic Neurons to Encode Slow Signals

Fundamental properties of phasic firing neurons are usually characterized in a noise-free condition. In the absence of noise, phasic neurons exhibit Class 3 excitability, which is a lack of repetitive firing to steady current injections. For time-varying inputs, phasic neurons are band-pass filters or slope detectors, because they do not respond to inputs containing exclusively low frequencies or shallow slopes. However, we show that in noisy conditions, response properties of phasic neuron models are distinctly altered. Noise enables a phasic model to encode low-frequency inputs that are outside of the response range of the associated deterministic model. Interestingly, this seemingly stochastic-resonance (SR) like effect differs significantly from the classical SR behavior of spiking systems in both the signal-to-noise ratio and the temporal response pattern. Instead of being most sensitive to the peak of a subthreshold signal, as is typical in a classical SR system, phasic models are most sensitive to the signal''s rising and falling phases where the slopes are steep. This finding is consistent with the fact that there is not an absolute input threshold in terms of amplitude; rather, a response threshold is more properly defined as a stimulus slope/frequency. We call the encoding of low-frequency signals with noise by phasic models a slope-based SR, because noise can lower or diminish the slope threshold for ramp stimuli. We demonstrate here similar behaviors in three mechanistic models with Class 3 excitability in the presence of slow-varying noise and we suggest that the slope-based SR is a fundamental behavior associated with general phasic properties rather than with a particular biological mechanism.     

A novel mechanism for irregular firing of a neuron in response to periodic stimulation: irregularity in the absence of noise

Irregular firing of action potentials (AP's) is a characteristic feature of neurons in the brain. The variability has been attributed to noise from various sources. This study illustrates an alternative mechanism, namely, deterministic irregularity within a model of ionic conductances. Specifically, a model based on modern measurements of the Na⁺ and K⁺ current components from the squid giant axon fires irregularly in response to a continuous train of near-threshold current pulses. The interspike interval histogram from these simulations is multi-modal, a result which in other systems has been attributed to stochastic resonance. Moreover, the simulations exhibited short burst of spikes followed by relatively long quiescent periods, a result suggestive of patterned input to the model even though the input consisted of a train of regularly spaced current pulses. The variability of firing is attributable to variations in AP parameters, in particular AP amplitude. The action potential for squid giant axons is not all-or-none. Rather, it is fundamentally a continuous function of stimulus amplitude. That is, the membrane lacks a threshold. Variation in AP amplitude, and to a lesser extent, AP duration, can produce variations in the time to a subsequent AP, which represents a paradigm shift for understanding irregular neuronal firing. The emphasis is not as much on events prior to an AP as it is on the AP's themselves.