Third-order reverse correlation analysis of muscle spindle primary afferent fiber responses to random muscle stretch

 The response of primary muscle spindle afferent fibers to muscle stretch is nonlinear. Now spindle responses (trains of action potentials) to band-limited Gaussian white noise length perturbations of the gastrocnemius muscles (input signal) are described in cats. The input noise upper cutoff frequency was clearly above the frequency range of physiological length changes in cat hindleg muscles. The input–output relation was analyzed by means of peri-spike averages (PSAs), which could be shown to correspond to the kernels of Wiener’s white noise approach to systems identification. The present approach (the reverse correlation analysis) was applied up to the third order. An experiment consisted of two recordings: one (the source recording) to determine PSAs and the other (the test recording) to provide an input signal for predicting responses. The predictions of different orders were compared with the actual neuronal response (the observation) of the test recording. Four different approximation procedures were developed to adapt prediction and observation and to determine weighting factors for the predictions of different orders. The approximations also yielded the value of the power density P of the input noise signal: at a variety of stimulus parameters, P from approximations had the same magnitude as P determined directly from the input signal amplitude spectrum. The prediction of a sequence of action potentials improved the higher the order of components. 37 of 42 action potentials of a test recording (the observation) could be confidently predicted from PSAs or kernels. Compared with the size of the linear first-order prediction curve, the relative sizes of the second and third-order prediction curves were: 1.0 : 0.47 : 0.26. Received: 15 November 1994/Accepted in revised form: 23 May 1995     

Band-limited white noise stimulation and reverse correlation analysis in the prediction of impulse responses of encoder models

A method is described to test the predictability of impulse responses from responses to Gaussiandistributed random stimulation by means of the reverse correlation analysis. In addition, this analysis is tested as to whether it can handle responses of nonlinear systems to random inputs of strongly limited frequency content, which is often the case in data from physiological experiments. The basis for all computation is a simple backward averaging (peri-spike averaging, Istorder PSA) of the noise input triggered from the output pulsatile events, which was extended to two-dimensional peri-spike averaging (2nd-order PSA). These functions were shown to represent the 1st- and 2nd-order Wiener kernel and were taken to calculate the 1st-and 2nd-order response predictions to a given short random test sequence. Different models of impulse-initiating mechanisms were tested for their expression of nonlinearities in these PSAs. Output impulse densities of test sequence (the observed response) could be fairly well approximated by the result of the computations (the predicted response). The difference between observation and prediction was evaluated and expressed as the mean-least squares error. In some of the data the 2nd-order kernel seems sufficient to account for the major nonlinear component, in others, kernels of orders higher than two.     

Reverse correlation analysis of the stretch response of primary muscle spindle afferent fibers

The nonlinear responses of deefferented primary muscle spindle afferent fibers to muscle stretching consisted of a train of action potentials which was analyzed when random changes in muscle length (band-limited gaussian white noise) were applied in cats. The upper cutoff frequency of the applied noise (the source stimulus) was varied between 1.6 and 570 Hz; the amplitude of the random input was varied between 0.002 and 1.2 mm. In a previous report the reverse correlation of 1st and 2nd order was studied for its ability to analyze data of a continuous input signal and pulsatile events in the output. Computations of the Wiener kernelsh ₁ andh ₂ or their equivalents, the perispike averages of the 1st and 2nd order, were computed from the random stretch responses of muscle-spindle afferents. Then the 1st- and the 2ndorder predictions and the summation of both to random muscle stretch was estimated. A general finding was that the 1st-order component was approximately 10 times that of the 2nd-order component, when both were combined in approximation procedures to give the closest prediction of observed responses to random test stimuli. The approximation was poor when the source stimulus was less than 0.03 mm and improved when it was greater. With the increase in the upper cutoff frequency of the random source input, the approximation worsened continuously. Predictions to ramp-and-hold stimuli were computed, as well as responses to random stimulation. Limiting the upper cutoff frequency did not diminish the value of the techniques applied.     

The transducer and encoder of frog muscle spindles are essentially nonlinear. Physiological conclusions from a white-noise analysis

1. Nonlinear second order white-noise analysis has been applied to the isolated frog muscle spindle. Power (²) of the Gaussian white noise (GWN) and the average prestretch level L were varied and the response of both the isolated receptor potential (transducer) and the action potential (encoder) level were analysed. 2. The standard white-noise method is briefly presented. Particular emphasis, however, is put on the limitations in the range of validity of the method and, consequently, on the use and interpretation of the kernels as a Wiener model. Conclusions in the present paper are within this frame and are mainly of qualitative nature. 3. The analysis reveals that the nonlinear contributions of the model are essential for approximating physiological results, thus ruling out purely linear modelling for this receptor organ. 4. The dependence of the transducer kernels on are compatible with the behaviour of a rectifier. Rectification is represented by the lack of hyperpolarization within the isolated receptor potential and is enhanced by the substantial memory in the linear and nonlinear kernels as demonstrated by their extent in time. This is equivalent to low power in high frequencies of the response. Obviously, the hyperpolarizing potentials following each spike counteract the long transducer memory. 5. At the encoder level the memory of the system is strongly reduced. This is achieved by using predominantly high frequency components of the receptor potential for triggering the process of impulse generation, and by the precise coupling and high frequency content of the impulses. This coupling precision is possible because of the sensitivity of the spike-generating mechanism to steep rising transients of the receptor potential and also owing to the reduction in transducer memory by the hyperpolarizing afferpotentials. 6. The preference given to the high frequency components is also read from the structure of the second order transducer kernel and from both the linear and the second order encoder kernels, which allows the most effective input waveform for triggering action potentials to be determined. 6. When the operating point is changed to higher prestretch values, kernel heights increase strongly implying higher response strength of the muscle spindle. The kernel structure is changed as well in the direction of reducing the effective memory already at the level of the receptor potentials, probably a means to prevent too high depolarization values.     

Spike discharges of skeletomotor neurons during random noise modulated transmembrane current stimulation and muscle stretch

 Spike discharges of skeletomotor neurons innervating triceps surae muscles elicited by white noise modulated transmembrane current stimulation and muscle stretch were studied in decerebrated cats. The white noise modulated current intensity ranged from 4.3 to 63.2 nA peak-to-peak, while muscle stretches ranged from 100 μm to 4.26 mm peak-to-peak. The neuronal responses were studied by averaging the muscle length records centered at the skeletomotor action potentials (peri-spike average, PSA) and by Wiener analysis. Skeletomotor spikes appeared after a sharp peak in PSA of the injected current, preceded by a longer-lasting smaller wavelet of either depolarizing or hyperpolarizing direction. The PSA amplitude was not related to the injected current amplitude nor showed any differences related to the motor unit type. The PSA amplitudes were virtually independent of the stretching amplitude σ, after an initial increase with stretching amplitudes in the range of 15–40 μm (S.D.), or 100–270 μm peak-to-peak.Analyses of cross-spectra indicated a small or absent increase in gain with frequency in response to injected current, but about 20 dB/decade in the range 10–100 Hz in response to muscle stretch. The peaks of both Wiener kernels in response to current injection appear to decrease with the amplitude of injected current, but this decrease was not statistically significant. The narrow first-order kernels suggest that the transfer function between the current input and spike discharge is lowpass with a wide passband, i.e. there is very little change in dynamics. The values of the second-order kernels appear to be nonzero only along the main diagonal. This is characteristic of a simple Hammerstein type cascade, i.e. a zero memory nonlinearity followed by a linear system. Small values of second-order kernels away from the origin and narrow first-order kernels suggest that the linear cascade contributes very little to the overall dynamic response.In contrast to Wiener kernels found in response to current injection, the Wiener kernels in response to stretch showed a decreasing trend with stretch amplitude. The size of the second-order kernels decreased to a somewhat larger extent with input amplitude than that of the first-order kernels, indicating an amplitude-dependent nonlinearity. Overall, the transformation between length and spike output was described as an LNNL cascade with second-order nonlinearities. Received: 1 April 1993/Accepted in revised form: 24 March 1994     

Time coupling of skeletomotor discharges in response to pseudo-random transsynaptic and transmembrane stimulation

Firing pattern of skeletomotor neurones innervating triceps surae muscles in response to pseudorandom muscle stretching and white noise modulated transmembrane current stimulation was investigated in decerebrate cats. Pseudo-random muscle stretching (upper cut-off frequency 60 Hz, amplitude (standard deviation) ranging from 18.5 m to 40 m) was applied to triceps surae muscles. Membrane potential changes and action potentials of skeletomotor neurones were recorded intracellularly. White noise modulated current was applied through the same (recording) microelectrode. Sequences of ten identical 5 s periods of either muscle stretching or transmembrane current stimulation were applied. Skeletomotor neurones belonging to slow motor units (rheobase less than 8.5 nA) generated action potentials in response to both pseudo-random muscle stretching and transmembrane current stimulation, while firing threshold of those belonging to fast motor units could not be reached by the muscle stretches applied. Peri-spike averaging of muscle length and injected current records showed that the action potentials appeared at the peak of either depolarizing current wave or muscle stretching both preceded by a change in opposite direction (the spikes coinciding with the peak in muscle length PSA being actually elicited by muscle spindle action potentials triggered at the moment of the peak stretching velocity). Time coupling of action potentials occurred during both muscle stretching and transmembrane stimulation, being more tight in the latter case as well as when larger amplitudes of the stimuli were applied. It is supposed that discharges from muscle spindle primary endings phase-locked to small pseudo-random muscle length changes may, due to the time coupling of skeletomotor action potentials, provoke a synchronous firing of skeletomotor neurones, mostly of those belonging to slow motor units. Possible effects of such a firing pattern on the resulting muscle reflex contraction and the stretch reflex stability as well as a possibility of it being provoked by fusimotor discharges are discussed.     

Understanding auditory spectro-temporal receptive fields and their changes with input statistics by efficient coding principles

Spectro-temporal receptive fields (STRFs) have been widely used as linear approximations to the signal transform from sound spectrograms to neural responses along the auditory pathway. Their dependence on statistical attributes of the stimuli, such as sound intensity, is usually explained by nonlinear mechanisms and models. Here, we apply an efficient coding principle which has been successfully used to understand receptive fields in early stages of visual processing, in order to provide a computational understanding of the STRFs. According to this principle, STRFs result from an optimal tradeoff between maximizing the sensory information the brain receives, and minimizing the cost of the neural activities required to represent and transmit this information. Both terms depend on the statistical properties of the sensory inputs and the noise that corrupts them. The STRFs should therefore depend on the input power spectrum and the signal-to-noise ratio, which is assumed to increase with input intensity. We analytically derive the optimal STRFs when signal and noise are approximated as Gaussians. Under the constraint that they should be spectro-temporally local, the STRFs are predicted to adapt from being band-pass to low-pass filters as the input intensity reduces, or the input correlation becomes longer range in sound frequency or time. These predictions qualitatively match physiological observations. Our prediction as to how the STRFs should be determined by the input power spectrum could readily be tested, since this spectrum depends on the stimulus ensemble. The potentials and limitations of the efficient coding principle are discussed.     

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.     

Principal dynamic mode analysis of action potential firing in a spider mechanoreceptor

The encoding of mechanical stimuli into action potentials in two types of spider mechanoreceptor neurons is modeled by use of the principal dynamic modes (PDM) methodology. The PDM model is equivalent to the general Wiener–Bose model and consists of a minimum set of linear dynamic filters (PDMs), followed by a multivariate static nonlinearity and a threshold function. The PDMs are obtained by performing eigen-decomposition of a matrix constructed using the first-order and second-order Volterra kernels of the system, which are estimated by means of the Laguerre expansion technique, utilizing measurements of pseudorandom mechanical stimulation (input signal) and the resulting action potentials (output signal). The static nonlinearity, which can be viewed as a measure of the probability of action potential firing as a function of the PDM output values, is computed as the locus of points of the latter that correspond to output action potentials. The performance of the model is assessed by computing receiver operating characteristic (ROC) curves, akin to the ones used in decision theory and quantified by computing the area under the ROC curve. Three PDMs are revealed by the analysis. The first PDM exhibits a high-pass characteristic, illustrating the importance of the velocity of slit displacement in the generation of action potentials at the mechanoreceptor output, while the second and third PDMs exhibit band-pass and low-pass characteristics, respectively. The corresponding three-input nonlinearity exhibits asymmetric behavior with respect to its arguments, suggesting directional dependence of the mechanoreceptor response on the mechanical stimulation and the PDM outputs, in agreement to our findings from a previous study (Ann Biomed Eng 27:391–402, 1999). Differences between the Type A and B neurons are observed in the zeroth-order Volterra kernels (related to the average firing), as well as in the magnitudes of the second and third PDMs that perform band-pass and low-pass processing of the input signal, respectively.     

Sites of action of a peptide neurohormone that controls hindgut muscle activity in the cockroach Leucophaea maderae

A peptide neurohormone from the brain and nervous system of the Madeira cockroach Leucophaea maderae has stimulating effects on both the mechanical and electrical events of hindgut visceral muscle. The peptide initiated action potentials at silent recording sites in the circular muscles of the rectum after prior treatment with tetrodotoxin (10⁻⁶ g/ml). The neurohormone also caused an increase in the amplitude and frequency of spontaneous postsynaptic potentials. However, the isolated hindgut failed to respond to the neurohormone after depolarization in high potassium saline solutions. Both the potassium contracture and the action of the neurohormone were calcium dependent.Although some hindguts were responsive to the neurohormone in a Ca free medium, such preparations failed to respond in 0·5 mM EGTA. Moreover, 1 mM Mn blocked the action of the peptide. The sodium ion was also essential for effective hormone action. These results suggest the presence of a loosely bound source of Ca at the surface of muscle membranes that in some way interacts with the neurohormone to change muscle excitability.     

Intrinsic dendritic filtering gives low-pass power spectra of local field potentials

The local field potential (LFP) is among the most important experimental measures when probing neural population activity, but a proper understanding of the link between the underlying neural activity and the LFP signal is still missing. Here we investigate this link by mathematical modeling of contributions to the LFP from a single layer-5 pyramidal neuron and a single layer-4 stellate neuron receiving synaptic input. An intrinsic dendritic low-pass filtering effect of the LFP signal, previously demonstrated for extracellular signatures of action potentials, is seen to strongly affect the LFP power spectra, even for frequencies as low as 10 Hz for the example pyramidal neuron. Further, the LFP signal is found to depend sensitively on both the recording position and the position of the synaptic input: the LFP power spectra recorded close to the active synapse are typically found to be less low-pass filtered than spectra recorded further away. Some recording positions display striking band-pass characteristics of the LFP. The frequency dependence of the properties of the current dipole moment set up by the synaptic input current is found to qualitatively account for several salient features of the observed LFP. Two approximate schemes for calculating the LFP, the dipole approximation and the two-monopole approximation, are tested and found to be potentially useful for translating results from large-scale neural network models into predictions for results from electroencephalographic (EEG) or electrocorticographic (ECoG) recordings.     

Local development of action potentials in slow muscle fibres after complete or partial denervation.

Pyriformis muscles of Rana temporaria were completely or partially denervated by cutting the sciatic nerve or some of the small nerve branches entering the muscle. One stimulating and one to three recording microelectrodes were inserted along the fibres in order to compare the electrical activity at these points. In an early period following denervation action potentials of variable size and shape could be observed; these action potentials were often composed of two, sometimes of three or four, components. The size of individual components depended on the position of the recording microelectrode. Individual components could occasionally be triggered separately by adjusting the strength of the stimulating current pulse; propagation of these "all or none" responses was absent. In other fibres one component of the action potential could trigger another one several millimetres apart, thus indicating propagation. Conduction velocities were approximately 0.4 m/s. In partially denervated slow fibres, endplate potentials were confined to one lateral segment of the fibres, while the action potential occupied the denervated part of the membrane. The amplitudes of endplate and action potentials varied inversely with distance. Rough estimates of the length constant of the slow fibre membrane were calculated from the spatial decay of action potentials, endplate potentials and hyperpolarizing electrotonic potentials; mean values obtained were 2.5, 4.8 and 7.7 mm respectively. The results suggest that following denervation Na channels are built into discrete areas of the slow fibre membrane and that this process depends on the amount of denervation in individual fibres.     

How to record a million synaptic weights in a hippocampal slice

A key step toward understanding the function of a brain circuit is to find its wiring diagram. New methods for optical stimulation and optical recording of neurons make it possible to map circuit connectivity on a very large scale. However, single synapses produce small responses that are difficult to measure on a large scale. Here I analyze how single synaptic responses may be detectable using relatively coarse readouts such as optical recording of somatic calcium. I model a network consisting of 10,000 input axons and 100 CA1 pyramidal neurons, each represented using 19 compartments with voltage-gated channels and calcium dynamics. As single synaptic inputs cannot produce a measurable somatic calcium response, I stimulate many inputs as a baseline to elicit somatic action potentials leading to a strong calcium signal. I compare statistics of responses with or without a single axonal input riding on this baseline. Through simulations I show that a single additional input shifts the distribution of the number of output action potentials. Stochastic resonance due to probabilistic synaptic release makes this shift easier to detect. With ~80 stimulus repetitions this approach can resolve up to 35% of individual activated synapses even in the presence of 20% recording noise. While the technique is applicable using conventional electrical stimulation and extracellular recording, optical methods promise much greater scaling, since the number of synapses scales as the product of the number of inputs and outputs. I extrapolate from current high-speed optical stimulation and recording methods, and show that this approach may scale up to the order of a million synapses in a single two-hour slice-recording experiment.     

Giant smooth muscle cells of Beroe. Ultrastructure, innervation, and electrical properties

Beroe muscle fibers are single cells which may be 20-40 micrometer in diameter in mature specimens. Longitudinal muscles may be 6 cm or more long. There is no striation pattern and the muscles were observed to contract in a tonic fashion when stretched. They are innervated by a nerve net, and external recording revealed what are probably nerve net impulses. Intracellular stimulation of the muscles themselves was found to initiate large propagating action potentials which were recorded intracellularly. The action potentials were insensitive to tetrodotoxin (10(-5) g/ml), tetraethylammonium ions (50 mM), MnCl2 (25 mM), and low concentrations of verapamil (2 X 10(-6) g/ml). Full-size action potentials were recorded in sodium- or calcium-deficient salines, but were small and graded in salines deficient in both sodium and calcium. Cable analysis yielded mean values for lambda (1.95 mm), Ri (154 omega cm), Rm (9,253 omega cm2), and tau m (13.9 ms). The conduction velocity depended primarily on fiber diameter and maximum rate of rise of the action potential and could be predicted from the theoretical analysis of Hunter et al. (1975 Prog. Biophys. Mol. Biol. 30: 99-144). The calculated membrane capacity (less than microF/cm2) indicates little infolding of the surface membrane, a conclusion which is in agreement with anatomical studies.     

Observations on phase-locking within the response of primary muscle spindle afferents to pseudo-random stretch

In order to uncover encoder properties of primary muscle spindle afferent fibers, time coupling (phase-locking) of action potentials on cyclic muscle stretch was studied by means of pseudo-random noise. In cats Ia action potentials were recorded from dorsal root filaments and the gastrocnemius muscles of one hind leg were stretched. The stimulus time course was a determined sequence of randomly varying muscle length which could be applied repeatedly (sequence duration 0.6 or 20 s). The noise amplitude (standard deviation of displacements) was varied between 5 and 300 m, the upper cut-off frequency of noise f _c was varied between 20 and 100 Hz. The responses to the consecutive pseudo-random noise cycles were displayed as raster diagrams and cycle histograms. Phaselocking characterized the responses at all noise amplitudes outside the near threshold range (>10 m). The higher and f _c , the stronger was the phase-locking of impulses on the stretch. When and f _c were selected to achieve high mean stretch velocities of about 500 mm/s, phase-locking was as precise as 0.15 ms, measured as the variability of spike occurrences with respect to stretch. The rasters obtained with low noise amplitudes (<40 m) showed a loose phase-locking and this gave insight into underlying mechanisms: The elicitation of action potentials caused by dynamic stretch can be prevented by a post-spike depression of excitability. This disfacilitation was very effective in counteracting weak stretch components within the random sequence and less effective or even missing when relatively strong stretch components could force the spike elicitation. This led to the reestablishment of phase-locked patterns. The results were discussed in relation to the known encoder models.     

Diapause termination and egg hatch in the bird cherry aphid, Rhopalosiphum padi

Conclusion TastePROBE is a convenient and flexible electronic circuit designed to record action potentials from taste sensilla of insects. It facilitates the recording of slow potentials arising in taste sensilla, improves the signal to noise ratio, and preserves spike shapes. This new amplifier design combines excellent signal to noise ratio with complete compatibility as regards existing electrophysiological equipment.DC recordings have higher information content than filtered recordings. With DC recordings, spike shapes are not modified and thus better sorting is possible. Moreover, slow variations in the transepithelial potential (i.e. less than 10 Hz) are preserved. Both aspects are of considerable importance when studying the physiology of taste receptors.     

Effect of Voltage Sensitive Fluorescent Proteins on Neuronal Excitability

Fluorescent protein voltage sensors are recombinant proteins that are designed as genetically encoded cellular probes of membrane potential using mechanisms of voltage-dependent modulation of fluorescence. Several such proteins, including VSFP2.3 and VSFP3.1, were recently reported with reliable function in mammalian cells. They were designed as molecular fusions of the voltage sensor of Ciona intestinalis voltage sensor containing phosphatase with a fluorescence reporter domain. Expression of these proteins in cell membranes is accompanied by additional dynamic membrane capacitance, or “sensing capacitance”, with feedback effect on the native electro-responsiveness of targeted cells. We used recordings of sensing currents and fluorescence responses of VSFP2.3 and of VSFP3.1 to derive kinetic models of the voltage-dependent signaling of these proteins. Using computational neuron simulations, we quantitatively investigated the perturbing effects of sensing capacitance on the input/output relationship in two central neuron models, a cerebellar Purkinje and a layer 5 pyramidal neuron. Probe-induced sensing capacitance manifested as time shifts of action potentials and increased synaptic input thresholds for somatic action potential initiation with linear dependence on the membrane density of the probe. Whereas the fluorescence signal/noise grows with the square root of the surface density of the probe, the growth of sensing capacitance is linear. We analyzed the trade-off between minimization of sensing capacitance and signal/noise of the optical read-out depending on kinetic properties and cellular distribution of the probe. The simulation results suggest ways to reduce capacitive effects at a given level of signal/noise. Yet, the simulations indicate that significant improvement of existing probes will still be required to report action potentials in individual neurons in mammalian brain tissue in single trials.     

cgDNA: a software package for the prediction of sequence-dependent coarse-grain free energies of B-form DNA

cgDNA is a package for the prediction of sequence-dependent configuration-space free energies for B-form DNA at the coarse-grain level of rigid bases. For a fragment of any given length and sequence, cgDNA calculates the configuration of the associated free energy minimizer, i.e. the relative positions and orientations of each base, along with a stiffness matrix, which together govern differences in free energies. The model predicts non-local (i.e. beyond base-pair step) sequence dependence of the free energy minimizer. Configurations can be input or output in either the Curves+ definition of the usual helical DNA structural variables, or as a PDB file of coordinates of base atoms. We illustrate the cgDNA package by comparing predictions of free energy minimizers from (a) the cgDNA model, (b) time-averaged atomistic molecular dynamics (or MD) simulations, and (c) NMR or X-ray experimental observation, for (i) the Dickerson–Drew dodecamer and (ii) three oligomers containing A-tracts. The cgDNA predictions are rather close to those of the MD simulations, but many orders of magnitude faster to compute. Both the cgDNA and MD predictions are in reasonable agreement with the available experimental data. Our conclusion is that cgDNA can serve as a highly efficient tool for studying structural variations in B-form DNA over a wide range of sequences.     

Evolution of surface electromyography: From muscle electrophysiology towards neural recording and interfacing

Surface electromyography (EMG) comprises a recording of electrical activity from the body surface generated by muscle fibres during muscle contractions. Its characteristics depend on the fibre membrane potentials and the neural activation signal sent from the motor neurons to the muscles. EMG has been classically used as the primary investigation tool in kinesiology studies in a variety of applications. More recently, surface EMG techniques have evolved from single-channel methods to high-density systems with hundreds of electrodes. High-density EMG recordings can be deconvolved to estimate the discharge times of spinal motor neurons innervating the recorded muscles, with algorithms that have been developed and validated in the last two decades. Within limits and with some variability across muscles, these techniques provide a non-invasive method to study relatively large populations of motor neurons in humans. Surface EMG is thus evolving from a peripheral measure of muscle electrical activity towards a neural recording and neural interfacing signal. These advances in technology have had a major impact on our fundamental understanding of the neural control of movement and have exposed new perspectives in neurotechnologies. Here we provide an overview and perspective of modern EMG technology, as derived from past achievements, and its impact in neurophysiology and neural engineering.