Sensory transduction in an insect mechanoreceptor: Linear and nonlinear properties

Mechanotransduction in the femoral tactile spine of the cockroach, Periplaneta americana, was examined as a function of displacement of the spine axially in its socket. Linear behaviour was analyzed by measurement of the frequency response function between displacement and action potential output using sinusoidal stimulation and random noise stimulation. The measured frequency response functions can be well fitted by a relationship which is a fractional power of complex frequency. This power was close to 0.5 for all experiments. To distinguish between the effects of nonlinearity and of inherent variability, the averaged responses of the preparation to repeated sequences of pseudorandom noise were compared to those from experiments in which continuous pseudorandom noise were used. The lack of sensitivity of the coherence function to these two methods of measurement suggests that mechanical stimuli are encoded into action potentials with a large signal-to-noise ratio. The low value of the coherence function which is characteristics of insect mechanoreceptors is therefore due to the strong non-linearity of their responses. To investigate the nonlinear properties of transduction, the second-order frequency response function of the tactile spine was measured for random noise stimulation experiments. Two models of the transduction process were considered in which a linear element with memory was cascaded with a nonlinear element without memory in the two possible configurations. Comparison of the experimental second-order frequency response functions with predictions based upon these two models and the measured first-order frequency response suggests that the transduction mechanism can be modelled by a linear element, which may be associated with the viscoelastic properties of the dendritic tubular body, and a zeromemory nonlinearity, which is most likely to be rectification by the dendritic membrane.     

Computational simulation of the input-output relationship in hippocampal pyramidal cells

The precise mapping of how complex patterns of synaptic inputs are integrated into specific patterns of spiking output is an essential step in the characterization of the cellular basis of network dynamics and function. Relative to other principal neurons of the hippocampus, the electrophysiology of CA1 pyramidal cells has been extensively investigated. Yet, the precise input-output relationship is to date unknown even for this neuronal class. CA1 pyramidal neurons receive laminated excitatory inputs from three distinct pathways: recurrent CA1 collaterals on basal dendrites, CA3 Schaffer collaterals, mostly on oblique and proximal apical dendrites, and entorhinal perforant pathway on distal apical dendrites. We implemented detailed computer simulations of pyramidal cell electrophysiology based on three-dimensional anatomical reconstructions and compartmental models of available biophysical properties from the experimental literature. To investigate the effect of synaptic input on axosomatic firing, we stochastically distributed a realistic number of excitatory synapses in each of the three dendritic layers. We then recorded the spiking response to different stimulation patterns. For all dendritic layers, synchronous stimuli resulted in trains of spiking output and a linear relationship between input and output firing frequencies. In contrast, asynchronous stimuli evoked non-bursting spike patterns and the corresponding firing frequency input-output function was logarithmic. The regular/irregular nature of the input synaptic intervals was only reflected in the regularity of output inter-burst intervals in response to synchronous stimulation, and never affected firing frequency. Synaptic stimulations in the basal and proximal apical trees across individual neuronal morphologies yielded remarkably similar input-output relationships. Results were also robust with respect to the detailed distributions of dendritic and synaptic conductances within a plausible range constrained by experimental evidence. In contrast, the input-output relationship in response to distal apical stimuli showed dramatic differences from the other dendritic locations as well as among neurons, and was more sensible to the exact channel densities. Action Editor: Alain Destexhe     

Sensory transduction in an insect mechanoreceptor: extended bandwidth measurements and sensitivity to simulus strenght

The dynamic properties of sensory transduction in an insect mechanoreceptor, the femoral tactile spine of the cockroach, Periplaneta americana, have been studied by measurement of the frequency response function between randomly varying movement of the tactile spine and afferent action potentials from the sensory neuron which innervates it. The frequency response function of the mechanoreceptor has been characterized over a frequency range which is more than ten times larger than has previously been used for this preparation. Also the effects of varying the amplitude of the stimulating signal have been studied by the use of a range of input signal strengths from about 0.5 to 10 m R.M.S. displacement. The measured frequency response functions can all be well fitted by a theoretical relationship which is a fractional exponent of complex frequency, provided that the time delay caused by conduction of the action potentials from the sensory dendrite to the recording electrodes is taken into account. Under small signal conditions the exponent of complex frequency is close to 0.5 but with larger displacements its value decreases to about half this value. The overall sensitivity of the receptor, as measured by the gain of the frequency response function at a natural frequency of 1 radian/s, is not significantly altered by changes in the input movement amplitude, so that the receptor behaves linearly in this respect. However, the mean rate of action potential occurrence is not linearly related to input movement amplitude. These results are discussed in terms of current theories of sensory transduction and the possible role of tubular bodies in the dynamic behaviour of insect cuticular mechanoreceptors.     

A nonlinear cascade model for action potential encoding in an insect sensory neuron.

Action potential encoding in the cockroach tactile spine neuron can be represented as a single-input single-output nonlinear dynamic process. We have used a new functional expansion method to characterize the nonlinear behavior of the neural encoder. This method, which yields similar kernels to the Wiener method, is more accurate than the latter and is efficient enough to obtain reasonable kernels in less than 15 min using a personal computer. The input stimulus was band-limited white Gaussian noise and the output consisted of the resulting train of action potentials, which were unitized to give binary values. The kernels and the system input-output signals were used to identify a model for encoding comprising a cascade of dynamic linear, static nonlinear, and dynamic linear components. The two dynamic linear components had repeatable and distinctive forms with the first being low-pass and the second being high-pass. The static nonlinearity was fitted with a fifth-order polynomial function over several input amplitude ranges and had the form of a half-wave rectifier. The complete model gave a good approximation to the output of the neuron when both were subjected to the same novel white noise input signal.     

Interpreting Frequency Responses to Dose-Conserved Pulsatile Input Signals in Simple Cell Signaling Motifs

Many hormones are released in pulsatile patterns. This pattern can be modified, for instance by changing pulse frequency, to encode relevant physiological information. Often other properties of the pulse pattern will also change with frequency. How do signaling pathways of cells targeted by these hormones respond to different input patterns? In this study, we examine how a given dose of hormone can induce different outputs from the target system, depending on how this dose is distributed in time. We use simple mathematical models of feedforward signaling motifs to understand how the properties of the target system give rise to preferences in input pulse pattern. We frame these problems in terms of frequency responses to pulsatile inputs, where the amplitude or duration of the pulses is varied along with frequency to conserve input dose. We find that the form of the nonlinearity in the steady state input-output function of the system predicts the optimal input pattern. It does so by selecting an optimal input signal amplitude. Our results predict the behavior of common signaling motifs such as receptor binding with dimerization, and protein phosphorylation. The findings have implications for experiments aimed at studying the frequency response to pulsatile inputs, as well as for understanding how pulsatile patterns drive biological responses via feedforward signaling pathways.     

When Less Is More: Non-monotonic Spike Sequence Processing in Neurons

Fundamental response properties of neurons centrally underly the computational capabilities of both individual nerve cells and neural networks. Most studies on neuronal input-output relations have focused on continuous-time inputs such as constant or noisy sinusoidal currents. Yet, most neurons communicate via exchanging action potentials (spikes) at discrete times. Here, we systematically analyze the stationary spiking response to regular spiking inputs and reveal that it is generically non-monotonic. Our theoretical analysis shows that the underlying mechanism relies solely on a combination of the discrete nature of the communication by spikes, the capability of locking output to input spikes and limited resources required for spike processing. Numerical simulations of mathematically idealized and biophysically detailed models, as well as neurophysiological experiments confirm and illustrate our theoretical predictions.     

Modelling spatiotemporal properties of directionally sensitive multi-input single-output systems

The dynamics of directionally tuned linear multi-input single-output systems varies generally as a function of the spatial orientation of the inputs. A linear system receiving directionally specific inputs is represented by a linear combination of the respective input transfer functions. The input-output behaviour of such systems can be described by a vector transfer function which specifies the polarization directions of the system in real space. These directions, which can be either one (unidirectional vector transfer function) or two (bidirectional vector transfer function) but never three, are obtained by computing the eigenvectors and eigenvalues of the system matrix that is defined by the gain and phase values of the system's response to harmonic stimulation directed along three orthogonal directions in space. The spatial tuning behaviour is determined by the quadratic form associated with the system matrix. Neuronal systems with bidirectional vector transfer functions process input information in a plane-specific way and exhibit novel characteristics, very much different from those of systems with unidirectional vector transfer functions.     

Spike latency and jitter of neuronal membrane patches with stochastic Hodgkin-Huxley channels

Ion channel stochasticity can influence the voltage dynamics of neuronal membrane, with stronger effects for smaller patches of membrane because of the correspondingly smaller number of channels. We examine this question with respect to first spike statistics in response to a periodic input of membrane patches including stochastic Hodgkin-Huxley channels, comparing these responses to spontaneous firing. Without noise, firing threshold of the model depends on frequency—a sinusoidal stimulus is subthreshold for low and high frequencies and suprathreshold for intermediate frequencies. When channel noise is added, a stimulus in the lower range of subthreshold frequencies can influence spike output, while high subthreshold frequencies remain subthreshold. Both input frequency and channel noise strength influence spike timing. Specifically, spike latency and jitter have distinct minima as a function of input frequency, showing a resonance like behavior. With either no input, or low frequency subthreshold input, or input in the low or high suprathreshold frequency range, channel noise reduces latency and jitter, with the strongest impact for the lowest input frequencies. In contrast, for an intermediate range of suprathreshold frequencies, where an optimal input gives a minimum latency, the noise effect reverses, and spike latency and jitter increase with channel noise. Thus, a resonant minimum of the spike response as a function of frequency becomes more pronounced with less noise. Spike latency and jitter also depend on the initial phase of the input, resulting in minimal latencies at an optimal phase, and depend on the membrane time constant, with a longer time constant broadening frequency tuning for minimal latency and jitter. Taken together, these results suggest how stochasticity of ion channels may influence spike timing and thus coding for neurons with functionally localized concentrations of channels, such as in “hot spots” of dendrites, spines or axons.     

The Frequency Response, Coherence, and Information Capacity of Two Neuronal Models

Two neuronal models are analyzed in which subthreshold inputs are integrated either without loss (perfect integrator) or with a decay which follows an exponential time course (leaky integrator). Linear frequency response functions for these models are compared using sinusoids, Poisson-distributed impulses, or gaussian white noise as inputs. The responses of both models show the nonlinear behavior characteristic of a rectifier for sinusoidal inputs of sufficient amplitude. The leaky integrator shows another nonlinearity in which responses become phase locked to cyclic stimuli. Addition of white noise reduces the distortions due to phase locking. Both models also show selective attenuation of high-frequency components with white noise inputs. Input, output, and cross-spectra are computed using inputs having a broad frequency spectrum. Measures of the coherence and information transmission between the input and output of the models are also derived. Steady inputs, which produce a constant “carrier” rate, and intrinsic sources, which produce variability in the discharge of neurons, may either increase or decrease coherence; however, information transmission using inputs with a broad spectrum is generally increased by steady inputs and reduced by intrinsic variability.     

Analysis of the coding of a periodic movement of stretch receptors of an insect

A study has been made of the input-output relationships of the in situ stretch receptor organs at the tibio-femoral joint of the locust. Sinusoidal deformations of variable amplitudes and frequencies were applied at different angular levels of the tibia. Three units were mainly recorded with silver electrodes on the lateral femoral nerve of the insect. The experimental conditions for which the discharge was periodically abolished are described. The shape and the amplitude of the impulse frequency modulation signal were studied also in relation to the stimulus gradation applied at the input. This response was highly temperature dependent. A graphical representation of the gain is given by a Bode plot (1·7 dB/octave), but over the range of stimulus cycle frequencies the phase-advance of impulse frequency modulation was constant. In successive cycles, there was no phase relation for each spike, except when the response was limited to a single one. In this condition, the response seemed locked in phase over a small range of relatively high frequencies.     

Vestibular integrator neurons have quadratic functions due to voltage dependent conductances

The nonlinear properties of the dendrites of the prepositus hypoglossi nucleus (PHN) neurons are essential for the operation of the vestibular neural integrator that converts a head velocity signal to one that controls eye position. A novel system of frequency probing, namely quadratic sinusoidal analysis (QSA), was used to decode the intrinsic nonlinear behavior of these neurons under voltage clamp conditions. Voltage clamp currents were measured at harmonic and interactive frequencies using specific nonoverlapping stimulation frequencies. Eigenanalysis of the QSA matrix reduces it to a remarkably compact processing unit, composed of just one or two dominant components (eigenvalues). The QSA matrix of rat PHN neurons provides signatures of the voltage dependent conductances for their particular dendritic and somatic distributions. An important part of the nonlinear response is due to the persistent sodium conductance (g_NaP), which is likely to be essential for sustained effects needed for a neural integrator. It was found that responses in the range of 10 mV peak to peak could be well described by quadratic nonlinearities suggesting that effects of higher degree nonlinearities would add only marginal improvement. Therefore, the quadratic response is likely to sufficiently capture most of the nonlinear behavior of neuronal systems except for extremely large synaptic inputs. Thus, neurons have two distinct linear and quadratic functions, which shows that piecewise linear?+?quadratic analysis is much more complete than just piecewise linear analysis; in addition quadratic analysis can be done at a single holding potential. Furthermore, the nonlinear neuronal responses contain more frequencies over a wider frequency band than the input signal. As a consequence, they convert limited amplitude and bandwidth input signals to wider bandwidth and more complex output responses. Finally, simulations at subthreshold membrane potentials with realistic PHN neuron models suggest that the quadratic functions are fundamentally dominated by active dendritic structures and persistent sodium conductances.     

A nonlinear model of step responses in the cockroach tactile spine neuron

Rapid sensory adaptation in the cockroach tactile spine neuron has previously been associated with a labile threshold for action potentials, which changes with the membrane potential by a process involving two time constants. A feed-forward, variable-threshold model has previously been used to account for the frequency response function of the neuron when stimulated with small-signal, white-noise currents. Here, we used a range of accurately controlled steps of extracellular current to stimulate the neuron. The same model was able to predict the individual step responses and could also fit the entire set of step responses from a single neuron if an initial, saturating, static nonlinearity was included. These results indicate that the two-time-constant, variable-threshold model can account for most of the rapidly adapting behavior of the tactile spine neuron.     

Frequency response characteristics of an isolated photoreceptor

The transmembrane voltage change in response to light was studied in the barnacle photoreceptor by using sinusoidal and impulse changes in light intensity. The input-output relation is linear if the transmembrane voltage change does not exceed 10 mV. The frequency response is of low pass character with attenuation beginning at 1 cps. The system can best be represented by a third order transfer function consisting of a first order pole, a complex second order pole, and a transport delay. Lower temperature causes greater high frequency attenuation of the voltage response. Background illumination, depolarization, and wavelength do not affect the frequency response within the linear range. Beyond the linear range the elements of a differentiating process are introduced. This is probably due to a disproportionate increase in cell conductance.     

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.     

Multiple coherence of cerebral blood flow velocity in humans

The coherence function has been used in transfer function analysis of dynamic cerebral autoregulation to assess the statistical significance of spectral estimates of gain and phase frequency response. Interpretation of the coherence function and choice of confidence limits has not taken into account the intrinsic nonlinearity represented by changes in cerebrovascular resistance due to vasomotor activity. For small spontaneous changes in arterial blood pressure (ABP), the relationship between ABP and cerebral blood flow velocity (CBFV) can be linearized, showing that corresponding changes in cerebrovascular resistance should be included as a second input variable. In this case, the standard univariate coherence function needs to be replaced by the multiple coherence, which takes into account the contribution of both inputs to explain CBFV variability. With the use of two different indicators of cerebrovascular resistance index [CVRI = ABP/CBFV and the resistance-area product (RAP)], multiple coherences were calculated for 42 healthy control subjects, aged 20 to 40 yr (28 +/- 4.6 yr, mean +/- SD), at rest in the supine position. CBFV was measured in both middle cerebral arteries, and ABP was recorded noninvasively by finger photoplethysmography. Results for the ABP + RAP inputs show that the multiple coherence of CBFV for frequencies <0.05 Hz is significantly higher than the corresponding values obtained for univariate coherence (P < 10(-5)). Corresponding results for the ABP + CVRI inputs confirm the principle of multiple coherence but are less useful due to the interdependence between CVRI, ABP, and CBFV. The main conclusion is that values of univariate coherence between ABP and CBFV should not be used to reject spectral estimates of gain and phase, derived from small fluctuations in ABP, because the true explained power of CBFV in healthy subjects is much higher than what has been usually predicted by the univariate coherence functions.     

Firing-rate models capture essential response dynamics of LGN relay cells

Firing-rate models provide a practical tool for studying signal processing in the early visual system, permitting more thorough mathematical analysis than spike-based models. We show here that essential response properties of relay cells in the lateral geniculate nucleus (LGN) can be captured by surprisingly simple firing-rate models consisting of a low-pass filter and a nonlinear activation function. The starting point for our analysis are two spiking neuron models based on experimental data: a spike-response model fitted to data from macaque (Carandini et al. J. Vis., 20(14), 1–2011, 2007), and a model with conductance-based synapses and afterhyperpolarizing currents fitted to data from cat (Casti et al. J. Comput. Neurosci., 24(2), 235–252, 2008). We obtained the nonlinear activation function by stimulating the model neurons with stationary stochastic spike trains, while we characterized the linear filter by fitting a low-pass filter to responses to sinusoidally modulated stochastic spike trains. To account for the non-Poisson nature of retinal spike trains, we performed all analyses with spike trains with higher-order gamma statistics in addition to Poissonian spike trains. Interestingly, the properties of the low-pass filter depend only on the average input rate, but not on the modulation depth of sinusoidally modulated input. Thus, the response properties of our model are fully specified by just three parameters (low-frequency gain, cutoff frequency, and delay) for a given mean input rate and input regularity. This simple firing-rate model reproduces the response of spiking neurons to a step in input rate very well for Poissonian as well as for non-Poissonian input. We also found that the cutoff frequencies, and thus the filter time constants, of the rate-based model are unrelated to the membrane time constants of the underlying spiking models, in agreement with similar observations for simpler models.     

A simple stochastic model of spatially complex neurons

A method for studying the coding properties of a multicompartmental integrate-and-fire neuron of arbitrary geometry is presented. Depolarization at each compartment evolves like a leaky integrator with an after-firing reset imposed only at the trigger zone. The frequency of firing at the steady-state regime is related to the properties of the multidimensional input. The decreasing variability of subthreshold depolarization from the dendritic tree to the trigger zone is shown for an input that is corrupted by a white noise. The role of a Poissonian noise is also investigated. The proposed method gives an estimate of the mean interspike interval that can be used to study the input output transfer function of the system. Both types of the stochastic inputs result in broadening the transfer function with respect to the deterministic case.     

Ghost resonance in a pool of heterogeneous neurons

We numerically study the subharmonic response of a heterogeneous pool of neurons to a pair of independent inputs. The neurons are stimulated with periodic pulse trains of frequencies f(1)=2 Hz and f(2)=3 Hz, and with inharmonic pulses whose frequencies f(1) and f(2) are equally shifted an amount Delta f. When both inputs are subthreshold, we find that the neurons respond at a frequency equal to f(2)-f(1) in the harmonic situation (Delta f=0), that increases linearly with Delta f in the inharmonic case. Thus the neurons detect a frequency not present in the input; this effect is termed "ghost resonance". When one of the inputs is slightly suprathreshold the ghost resonance persists, but responses related with the frequency of the suprathreshold input also emerge. This behavior must be taken into account in experimental studies of signal integration and coincidence detection by neuronal pools.     

Inhibitory synchrony as a mechanism for attentional gain modulation

Recordings from area V4 of monkeys have revealed that when the focus of attention is on a visual stimulus within the receptive field of a cortical neuron, two distinct changes can occur: The firing rate of the neuron can change and there can be an increase in the coherence between spikes and the local field potential (LFP) in the gamma-frequency range (30-50 Hz). The hypothesis explored here is that these observed effects of attention could be a consequence of changes in the synchrony of local interneuron networks. We performed computer simulations of a Hodgkin-Huxley type neuron driven by a constant depolarizing current, I, representing visual stimulation and a modulatory inhibitory input representing the effects of attention via local interneuron networks. We observed that the neuron's firing rate and the coherence of its output spike train with the synaptic inputs was modulated by the degree of synchrony of the inhibitory inputs. When inhibitory synchrony increased, the coherence of spiking model neurons with the synaptic input increased, but the firing rate either increased or remained the same. The mean number of synchronous inhibitory inputs was a key determinant of the shape of the firing rate versus current (f-I) curves. For a large number of inhibitory inputs (approximately 50), the f-I curve saturated for large I and an increase in input synchrony resulted in a shift of sensitivity-the model neuron responded to weaker inputs I. For a small number (approximately 10), the f-I curves were non-saturating and an increase in input synchrony led to an increase in the gain of the response-the firing rate in response to the same input was multiplied by an approximately constant factor. The firing rate modulation with inhibitory synchrony was highest when the input network oscillated in the gamma frequency range. Thus, the observed changes in firing rate and coherence of neurons in the visual cortex could be controlled by top-down inputs that regulated the coherence in the activity of a local inhibitory network discharging at gamma frequencies.