Firing Rate In Response Research Articles

On a Gaussian neuronal field model

Can we understand the dynamic behaviour of leaky integrate-and-fire (LIF) networks, which present the major, and possibly the only, analytically tractable tool we employ in computational neuroscience? To answer this question, here we present a theoretical framework on the spike activities of LIF networks by including the first order moment (mean firing rate) and the second order moment statistics (variance and correlation), based on a moment neuronal network (MNN) approach. The spike activity of a LIF network is approximated as a Gaussian random field and can reduce to the classical Wilson–Cowan–Amari (WCA) neural field if the variances vanish. Our analyses reveal several interesting phenomena of LIF networks. With a small clamped correlation and strong inhibition, the firing rate response function could be non-monotonic (not sigmoidal type), which can lead to interesting dynamics. For a feedforward and recurrent neuronal network, our setup allows us to prove that all neuronal spike activities rapidly synchronize, a well-known fact observed in both experiments and numerical simulations. We also present several examples of wave propagations in this field model. Finally, we test our MNN with the content-dependent working memory setting. The potential application of this random neuronal field idea to account for many experimental data is also discussed.

Predicting stimulus-locked single unit spiking from cortical local field potentials

The rapidly increasing use of the local field potential (LFP) has motivated research to better understand its relation to the gold standard of neural activity, single unit (SU) spiking. We addressed this in an in vivo, awake, restrained mouse auditory cortical electrophysiology preparation by asking whether the LFP could actually be used to predict stimulus-evoked SU spiking. Implementing a Bayesian algorithm to predict the likelihood of spiking on a trial by trial basis from different representations of the despiked LFP signal, we were able to predict, with high quality and fine temporal resolution (2 ms), the time course of a SU's excitatory or inhibitory firing rate response to natural species-specific vocalizations. Our best predictions were achieved by representing the LFP by its wide-band Hilbert phase signal, and approximating the statistical structure of this signal at different time points as independent. Our results show that each SU's action potential has a unique relationship with the LFP that can be reliably used to predict the occurrence of spikes. This "signature" interaction can reflect both pre- and post-spike neural activity that is intrinsic to the local circuit rather than just dictated by the stimulus. Finally, the time course of this "signature" may be most faithful when the full bandwidth of the LFP, rather than specific narrow-band components, is used for representation.

Rate dynamics of leaky integrate-and-fire neurons with strong synapses

Firing-rate models provide a practical tool for studying the dynamics of trial- or population-averaged neuronal signals. A wealth of theoretical and experimental studies has been dedicated to the derivation or extraction of such models by investigating the firing-rate response characteristics of ensembles of neurons. The majority of these studies assumes that neurons receive input spikes at a high rate through weak synapses (diffusion approximation). For many biological neural systems, however, this assumption cannot be justified. So far, it is unclear how time-varying presynaptic firing rates are transmitted by a population of neurons if the diffusion assumption is dropped. Here, we numerically investigate the stationary and non-stationary firing-rate response properties of leaky integrate-and-fire neurons receiving input spikes through excitatory synapses with alpha-function shaped postsynaptic currents for strong synaptic weights. Input spike trains are modeled by inhomogeneous Poisson point processes with sinusoidal rate. Average rates, modulation amplitudes, and phases of the period-averaged spike responses are measured for a broad range of stimulus, synapse, and neuron parameters. Across wide parameter regions, the resulting transfer functions can be approximated by a linear first-order low-pass filter. Below a critical synaptic weight, the cutoff frequencies are approximately constant and determined by the synaptic time constants. Only for synapses with unrealistically strong weights are the cutoff frequencies significantly increased. To account for stimuli with larger modulation depths, we combine the measured linear transfer function with the nonlinear response characteristics obtained for stationary inputs. The resulting linear–nonlinear model accurately predicts the population response for a variety of non-sinusoidal stimuli.

Background synaptic activity modulates spike train correlation

Event Abstract Back to Event Background synaptic activity modulates spike train correlation Ashok L. Kumar1*, Maurice J. Chacron2 and Brent Doiron3 1 Carnegie Mellon University, Center for the Neural Basis of Cognition, United States 2 McGill University, Department of Physiology, Canada 3 University of Pittsburgh, Department of Mathematics, United States Sensory neurons have many synaptic inputs, not all of which directly transmit sensory information. Instead, some inputs serve to modulate the neuronal response to relevant stimuli. One example of neural modulation is that the intensity of background synaptic input determines the postsynaptic neuron's leak conductance and membrane potential variability. This in turn controls the gain of its firing rate response to an excitatory stimulus [1]. State dependent gain control in sensory neurons is necessary for proper stimulus processing in low and high contrast environments, and allows attentional state to modulate signal transfer. Most work that characterizes the modulatory influence of background activity has focused on single cell responses, but it is reasonable to expect that such activity may also modulate population responses. We study how background activity shapes correlations between pairs of neurons using simulations, a theory from non-equilibrium statistical mechanics, and data recorded from electrosensory pyramidal neurons in weakly electric fish. We find that increasing background activity can decrease the output spike train correlation of two neurons over long time scales but increase pairwise synchrony on short time scales. We study these effects in simulated integrate-and-fire neurons. Using a linear response theory [2,3], we find that changes in correlation are related to single cell response properties including gain and integration timescale. We first demonstrate these changes in correlation using pairs of model neurons receiving partially correlated conductance-based synaptic activity. The neuron pairs received low or high levels of background activity, and spike train statistics were compared for these two states. We next demonstrate similar state-dependent changes in a recurrent network of neurons in which correlations arise through coupling. Finally, we test our predictions on simultaneously recorded extracellular spike data from primary sensory nucleus in the electrosensory system of weakly electric fish. We induce a state change in the neuronal response properties by driving the animal with stimuli mimicking a prey or a communication signal [4]. Consistent with our theoretical findings, the timescale and correlation of recorded neuron pairs vary depending on these processing states. The findings connect state-dependent changes in single cell response properties, such as firing rate gain, to changes in correlated activity in a neuronal population.

Modelling spike trains and extracting response latency with Bayesian binning

The peristimulus time histogram (PSTH) and the spike density function (SDF) are commonly used in the analysis of neurophysiological data. The PSTH is usually obtained by binning spike trains, the SDF being a (Gaussian) kernel smoothed version of the PSTH. While selection of the bin width or kernel size is often relatively arbitrary there have been recent attempts to remedy this situation ( Shimazaki and Shinomoto, 2007c,b,a). We further develop an exact Bayesian generative model approach to estimating PSTHs ( Endres et al., 2008) and demonstate its superiority to competing methods using data from early (LGN) and late (STSa) visual areas. We also highlight the advantages of our scheme’s automatic complexity control and generation of error bars. Additionally, our approach allows extraction of excitatory and inhibitory response latency from spike trains in a principled way, both on repeated and single trial data. We show that the method can be applied to data with high background firing rates and inhibitory responses (LGN) as well as to data with low firing rate and excitatory responses (STSa). Furthermore, we demonstrate on simulated data that our latency extraction method works for a range of signal-to-noise ratios and background firing rates. While further studies are needed to examine the sensitivity of our method to, for example, gradual changes in firing rate and adaptation, the current results suggest that Bayesian binning is a powerful method for the estimation of firing rate and the extraction response latency from neuronal spike trains.

Chronic hypoxia suppresses the CO2 response of solitary complex (SC) neurons from rats

We studied the effect of chronic hypobaric hypoxia (CHx; 10-11% O(2)) on the response to hypercapnia (15% CO(2)) of individual solitary complex (SC) neurons from adult rats. We simultaneously measured the intracellular pH and firing rate responses to hypercapnia of SC neurons in superfused medullary slices from control and CHx-adapted adult rats using the blind whole cell patch clamp technique and fluorescence imaging microscopy. We found that CHx caused the percentage of SC neurons inhibited by hypercapnia to significantly increase from about 10% up to about 30%, but did not significantly alter the percentage of SC neurons activated by hypercapnia (50% in control vs. 35% in CHx). Further, the magnitudes of the responses of SC neurons from control rats (chemosensitivity index for activated neurons of 166+/-11% and for inhibited neurons of 45+/-15%) were the same in SC neurons from CHx-adapted rats. This plasticity induced in chemosensitive SC neurons by CHx appears to involve intrinsic changes in neuronal properties since they were the same in synaptic blockade medium.

Immunohistochemical identification of K+ channel subunits in neurons from chemosensitive regions of rat brainstem

CO2‐sensitive neurons control breathing and are found in various regions of the brainstem. Neurons within these areas respond to CO2 with different percentages and different magnitudes of changes in firing rate responses to CO2. We propose that neuronal responses to CO2 are different because of differences in the ion channels expressed on neurons in these regions. The current study localized pH‐sensitive K+ channels in two different areas of the brainstem, the solitary tract (SC) (nucleus tractus solitarius, NTS, and dorsal motor nucleus, DMN) and locus coeruleus (LC). Brainstem slices (300µm) were fixed and thin sectioned (20‐40µm). Nissl staining was used to stain all cells and glial fibrillary acidic protein (GFAP)stained astrocytes. Neurons were identified as Nissl positive/GFAP negative. Sections were stained with antibodies for pH‐sensitive subunits of voltage‐sensitive (Kv1.4) and inwardly‐rectifying (Kir2.3 and Kir4.1) K+ channels. We found that Kv1.4 was prevalent on SC neurons (60‐90%), which stained less for Kir2.3 and 4.1 (30‐40%). In contrast, LC neurons stained similarly (50‐60%) for Kv1.4, Kir2.3 and Kir4.1. These data are consistent with an A current (inhibitable by 4 aminopyridine) playing a major role in the CO2 response of SC neurons but both an A current and Kir channels playing a role in the CO2 response of LC neurons.Supported by NIH R01 HL56683 (RWP) and T35 HL007805 (CM).

Characterization of the chemosensitive response of individual solitary complex neurons from adult rats

We studied the CO(2)/H(+)-chemosensitive responses of individual solitary complex (SC) neurons from adult rats by simultaneously measuring the intracellular pH (pH(i)) and electrical responses to hypercapnic acidosis (HA). SC neurons were recorded using the blind whole cell patch-clamp technique and loading the soma with the pH-sensitive dye pyranine through the patch pipette. We found that SC neurons from adult rats have a lower steady-state pH(i) than SC neurons from neonatal rats. In the presence of chemical and electrical synaptic blockade, adult SC neurons have firing rate responses to HA (percentage of neurons activated or inhibited and the magnitude of response as determined by the chemosensitivity index) that are similar to SC neurons from neonatal rats. They also have a typical response to isohydric hypercapnia, including decreased DeltapH(i), followed by pH(i) recovery, and increased firing rate. Thus, the chemosensitive response of SC neurons from adults is similar to the chemosensitive response of SC neurons from neonatal rats. Because our findings for adults are similar to previously reported values for neurons from neonatal rats, we conclude that intrinsic chemosensitivity is established early in development for SC neurons and is maintained throughout adulthood.

Statistical decision theory to relate neurons to behavior in the study of covert visual attention

Scrutiny of the numerous physiology and imaging studies of visual attention reveal that integration of results from neuroscience with the classic theories of visual attention based on behavioral work is not simple. The different subfields have pursued different questions, used distinct experimental paradigms and developed diverse models. The purpose of this review is to use statistical decision theory and computational modeling to relate classic theories of attention in psychological research to neural observables such as mean firing rate or functional imaging BOLD response, tuning functions, Fano factor, neuronal index of detectability and area under the receiver operating characteristic (ROC). We focus on cueing experiments and attempt to distinguish two major leading theories in the study of attention: limited resources model/increased sensitivity vs. selection/differential weighting. We use Bayesian ideal observer (BIO) modeling, in which predictive cues or prior knowledge change the differential weighting (prior) of sensory information to generate predictions of behavioral and neural observables based on Gaussian response variables and Poisson process neural based models. The ideal observer model can be modified to represent a number of classic psychological theories of visual attention by including hypothesized human attentional limited resources in the same way sequential ideal observer analysis has been used to include physiological processing components of human spatial vision (Geisler, W. S. (1989). Sequential ideal-observer analysis of visual discrimination. Psychological Review 96, 267–314.). In particular we compare new biologically plausible implementations of the BIO and variant models with limited resources. We find a close relationship between the behavioral effects of cues predicted by the models developed in the field of human psychophysics and their neuron-based analogs. Critically, we show that cue effects on experimental observables such as mean neural activity, variance, Fano factor and neuronal index of detectability can be consistent with the two major theoretical models of attention depending on whether the neuron is assumed to be computing likelihoods, log-likelihoods or a simple model operating directly on the Poisson variable. Change in neuronal tuning functions can also be consistent with both theories depending on whether the change in tuning is along the dimension being experimentally cued or a different dimension. We show that a neuron’s sensitivity appropriately measured using the area under the Receive Operating Characteristic curve can be used to distinguish across both theories and is robust to the many transformations of the decision variable. We provide a summary table with the hope that it might provide some guidance in interpreting past results as well as planning future studies.

Firing-rate response properties of spiking neurons: beyond the diffusion limit

Event Abstract Back to Event Firing-rate response properties of spiking neurons: beyond the diffusion limit Eilen Nordlie1*, Tom Tetzlaff1 and Gaute T. Einevoll1 1 Norwegian University of Life Sciences, Department of Mathematical Sciences and Technology, Norway Instantaneous firing rates are often used to describe either the compound spiking activity of neuron ensembles (population rate) or the trial-averaged response of individual neurons to multiple repetitions of the same stimulus. For the case of weak synapses and high input firing rates (diffusion limit), the (linear) rate response of an ensemble of independent neurons has been derived in previous studies for several integrate-and-fire (IaF) neuron models [1-5]. The assumption of weak synapses can however not always be justified: in the early visual pathway, for example, single action potentials emitted by retinal ganglion cells initiate response spikes in postsynaptic thalamic cells with a high probability [6]. Also within the cortex, a considerable fraction of synapses has moderately strong weights [7,8]. It is unclear to what extent the results obtained under the diffusion approximation can be generalised to these cases.Here, we numerically investigate the response properties of leaky IaFneurons receiving input spikes through arbitrarily strong excitatory (current-based alpha-) synapses. The input spike trains are modelled as Poisson point-processes with sinusoidal intensity. Modulation amplitudes and phases of the trial-averaged spike responses are measured for a broad range of stimulus modulation frequencies and synaptic weights.We show that the input-output rate transmission can be well modelled by a simple first-order low-pass filter (Fig.A). The dependence of the cutoff frequency fcon the synaptic weight w (Fig.B) is dominated by a sharp increase of fc at a critical weight wc (the minimal weight for which a single presynaptic action potential leads to a threshold crossing in the postsynaptic cell; dashed vertical line in Fig.B). In general, the cutoff frequencies are determined by the time constants τm and τs of both the membrane and the postsynaptic currents. The dependence on τm disappears if the synaptic weights are normalised by wc. The remaining dependence on τs is most prominent in the supercritical regime (w>wc). Acknowledgements:We acknowledge partial support by the Research Council of Norway (eNEURO, NOTUR) and the Honda Research Institute Europe. All simulations were carried out with the NEST simulation tool (see http://www.nest-initiative.org).

Transient Responses to Rapid Changes in Mean and Variance in Spiking Models

The mean input and variance of the total synaptic input to a neuron can vary independently, suggesting two distinct information channels. Here we examine the impact of rapidly varying signals, delivered via these two information conduits, on the temporal dynamics of neuronal firing rate responses. We examine the responses of model neurons to step functions in either the mean or the variance of the input current. Our results show that the temporal dynamics governing response onset depends on the choice of model. Specifically, the existence of a hard threshold introduces an instantaneous component into the response onset of a leaky-integrate-and-fire model that is not present in other models studied here. Other response features, for example a decaying oscillatory approach to a new steady-state firing rate, appear to be more universal among neuronal models. The decay time constant of this approach is a power-law function of noise magnitude over a wide range of input parameters. Understanding how specific model properties underlie these response features is important for understanding how neurons will respond to rapidly varying signals, as the temporal dynamics of the response onset and response decay to new steady-state determine what range of signal frequencies a population of neurons can respond to and faithfully encode.

Development of chemosensitivity in neurons from the nucleus tractus solitarii (NTS) of neonatal rats.

We studied the development of chemosensitivity during the neonatal period in rat nucleus tractus solitarii (NTS) neurons. We determined the percentage of neurons activated by hypercapnia (15% CO(2)) and assessed the magnitude of the response by calculating the chemosensitivity index (CI). There were no differences in the percentage of neurons that were inhibited (9%) or activated (44.8%) by hypercapnia or in the magnitude of the activated response (CI 164+/-4.9%) in NTS neurons from neonatal rats of all ages. To assess the degree of intrinsic chemosensitivity in these neurons we used chemical synaptic block medium and the gap junction blocker carbenoxolone. Chemical synaptic block medium slightly decreased basal firing rate but did not affect the percentage of NTS neurons that responded to hypercapnia at any neonatal age. However, in neonates aged <P10, but not in older neonates, chemical synaptic block medium increased CI. Carbenoxolone did not significantly alter the number of NTS neurons activated by hypercapnia in neonatal rats of any age. In summary, the response of NTS neurons from neonatal rats appears to be intrinsic and largely unchanged throughout early development. In young neonates (<P10) chemical synaptic input reduces the magnitude of the firing rate response to hypercapnia, but otherwise neither chemical synaptic input nor gap junctions significantly alter the percentage of NTS neurons that respond to hypercapnia or the magnitude of that response.

Contingent and non-contingent effects of low-dose ethanol on GABA neuron activity in the ventral tegmental area

Ventral tegmental area (VTA) GABA neurons appear to be critical regulators of mesocorticolimbic dopamine (DA) neurotransmission, which has been implicated in alcohol reward. The aim of this study was to evaluate the effects of low-dose “non-contingent” intravenous (IV) ethanol (0.01–0.1 g/kg) on VTA GABA neuron firing rate and synaptic responses, as well as VTA GABA neuron firing rate during low-dose “contingent” IV ethanol self-administration. Intravenous administration of 0.01–0.03 g/kg ethanol significantly increased VTA GABA neuron firing rate and afferent-evoked synaptic responses. In the runway self-administration paradigm, presentation of an olfactory cue (S+; almond extract) or no-cue (S−; no odor) in the Start box was paired with IV administration of low-dose ethanol (0.01 g/kg) or saline in the Target box. Runway excursion times decreased significantly in association during S+, and increased significantly during S− conditions. The firing rate of VTA GABA neurons markedly increased when rats received 0.01 g/kg IV ethanol in the Target box. VTA GABA neuron firing increased in the Start box of the runway in association with S+, but not S−. These findings demonstrate that VTA GABA neurons are activated by low-dose IV ethanol and that their firing rate increases in anticipation of ethanol reward.

Frequency-Independent Synaptic Transmission Supports a Linear Vestibular Behavior

The vestibular system is responsible for transforming head motion into precise eye, head, and body movements that rapidly stabilize gaze and posture. How do central excitatory synapses mediate behavioral outputs accurately matched to sensory inputs over a wide dynamic range? Here we demonstrate that vestibular afferent synapses in vitro express frequency-independent transmission that spans their in vivo dynamic range (5-150 spikes/s). As a result, the synaptic charge transfer per unit time is linearly related to vestibular afferent activity in both projection and intrinsic neurons of the vestibular nuclei. Neither postsynaptic glutamate receptor desensitization nor saturation affect the relative amplitude or frequency-independence of steady-state transmission. Finally, we show that vestibular nucleus neurons can transduce synaptic inputs into linear changes in firing rate output without relying on one-to-one calyceal transmission. These data provide a physiological basis for the remarkable linearity of vestibular reflexes.

Neuronal Activity of Mitral-Tufted Cells in Awake Rats During Passive and Active Odorant Stimulation

Odorants induce specific modulation of mitral/tufted (MT) cells' firing rate in the mammalian olfactory bulb (OB), inducing temporal patterns of neuronal discharge embedded in an oscillatory local field potential (LFP). While most studies have examined anesthetized animals, little is known about the firing rate and temporal patterns of OB single units and population activity in awake behaving mammals. We examined the firing rate and oscillatory activity of MT cells and LFP signals in behaving rats during two olfactory tasks: passive exposure (PE) and two-alternative (TA) choice discrimination. MT inhibitory responses are predominant in the TA task (76.5%), whereas MT excitatory responses predominate in the PE task (59.2%). Rhythmic discharge in the 12- to 100-Hz range was found in 79.0 and 68.9% of MT cells during PE and TA tasks, respectively. Most odorants presented in PE task increase rhythmic discharges at frequencies >50 Hz, whereas in TA, one of four odorants produced a modest increment <40 Hz. LFP oscillations were clearly modulated by odorants during the TA task, increasing their oscillatory power at frequencies centered at 20 Hz and decreasing power at frequencies >50 Hz. Our results indicate that firing rate responses of MT cells in awake animals are behaviorally modulated with inhibition being a prominent feature of this modulation. The occurrence of oscillatory patterns in single- and multiunitary discharge is also related to stimulation and behavioral context, while the oscillatory patterns of the neuronal population showed a strong dependence on odorant stimulation.

Au Naturel

Although adaptation is a ubiquitous property of neurons in the early visual pathway, the functional consequences in the natural visual environment are unknown. In this issue of Neuron, Mante et al. show, through a comprehensive set of in vivo experiments in the visual thalamus, that the basic functional mechanisms of adaptation that have been well studied with artificial probes capture the neuronal response in the natural environment and are predictable from properties of the visual scene that may be represented by local neural ensembles.

STIMULUS COMPETITION IN ATTENTION: A NEURAL MODEL OF VISUAL CORTEX AREA V4

When a monkey is presented simultaneously two stimuli in the receptive field of a neuron in the visual cortex area V4, the neuron firing rate response is intermediate between the neuron response when both stimuli are presented alone. This phenomenon is called stimulus competition. To study its basic underlying neural mechanisms, we calculate the neuron firing rate response to different stimulus configurations. We find that stimulus competition can arise from the neuron's response properties alone, but only for a limited set of stimulus pair parameters. Furthermore, network properties may be important in modifying the inputs so that competition may occur for much wider sets of stimulus pairs.

Functional Mechanisms Shaping Lateral Geniculate Responses to Artificial and Natural Stimuli

Functional models of the early visual system should predict responses not only to simple artificial stimuli but also to sequences of complex natural scenes. An ideal testbed for such models is the lateral geniculate nucleus (LGN). Mechanisms shaping LGN responses include the linear receptive field and two fast adaptation processes, sensitive to luminance and contrast. We propose a compact functional model for these mechanisms that operates on sequences of arbitrary images. With the same parameters that fit the firing rate responses to simple stimuli, it predicts the bulk of the firing rate responses to complex stimuli, including natural scenes. Further improvements could result by adding a spiking mechanism, possibly one capable of bursts, but not by adding mechanisms of slow adaptation. We conclude that up to the LGN the responses to natural scenes can be largely explained through insights gained with simple artificial stimuli.

Cyclic GMP alters the firing rate and thermosensitivity of hypothalamic neurons

The rostral hypothalamus, especially the preoptic-anterior hypothalamus (POAH), contains temperature-sensitive and -insensitive neurons that form synaptic networks to control thermoregulatory responses. Previous studies suggest that the cyclic nucleotide cGMP is an important mediator in this neuronal network, since hypothalamic microinjections of cGMP analogs produce hypothermia in several species. In the present study, immunohistochemisty showed that rostral hypothalamic neurons contain cGMP, guanylate cyclase (necessary for cGMP synthesis), and CNG A2 (an important cyclic nucleotide-gated channel). Extracellular electrophysiological activity was recorded from different types of neurons in rat hypothalamic tissue slices. Each recorded neuron was classified according to its thermosensitivity as well as its firing rate response to 2-100 microM 8-bromo-cGMP (a membrane-permeable cGMP analog). cGMP has specific effects on different neurons in the rostral hypothalamus. In the POAH, the cGMP analog decreased the spontaneous firing rate in 45% of temperature-sensitive and -insensitive neurons, an effect that is likely due to cGMP-enhanced hyperpolarizing K(+) currents. This decreased POAH activity could attenuate thermoregulatory responses and produce hypothermia during exposures to cool or neutral ambient temperatures. Although 8-bromo-cGMP did not affect the thermosensitivity of most POAH neurons, it did increase the warm sensitivity of neurons in other hypothalamic regions located dorsal, lateral, and posterior to the POAH. This increased thermosensitivity may be due to pacemaker currents that are facilitated by cyclic nucleotides. If some of these non-POAH thermosensitive neurons promote heat loss or inhibit heat production, then their increased thermosensitivity could contribute to cGMP-induced decreases in body temperature.

Contributions of CO2, pHi, and pHo to the signaling pathway of chemosensitive neurons in the locus coeruleus

Central chemosensitive neurons increase their firing rate (FR) upon exposure to hypercapnic acidosis (HA: 15% CO2, pHo 7.0). Multiple signaling pathways have been proposed to account for this increase including changes in Ca2+, CO2, pHi, and pHo. To determine the relative contributions of ΔpHi, ΔpHo, and CO2 to an increased FR, we used the rapid diffusion of a weak acid technique to clamp pHi. A change in pHi is believed to be the major signal in the chemosensitive (CS) response due to strong correlations between the degree of intracellular acidification and the increase of FR in HA (P&lt;0.05). In fact, an intracellular acidification (0.24 ± 0.04 U) with no change in pHo or CO2 elicits an increase in FR of 1.4 ± 0.5 Hz. However, this FR response can still be elicited without any change in pHi (1.4 ± 0.1 Hz). Only when both pHi and pHo are clamped, does hypercapnia not result in an increase in FR (0.4 ± 0.3 Hz in 5% CO2 to 0.16 ± 0.1 Hz in 15% CO2). Thus, the increase in FR seen upon HA with pHi clamped is likely due to the change in pHo (0.45 U) during the HA exposure. Our data suggest that ΔpHi and ΔpHo can both lead to an increase in FR in response to HA, while CO2 alone does not appear to have a significant contribution. It appears that ΔpHi or ΔpHo saturates the FR response because ΔFR with HA is not different than ΔFR with ΔpHi only or ΔpHo only. These data strongly support the multiple factors model of chemosensitivity. [NIH grants RO1 HL56683 and F32 HL80877].