Relationship of firing intervals of human motor units to the trajectory of post‐spike after‐hyperpolarization and synaptic noise.

Matthews, P. B. C.

doi:10.1113/jphysiol.1996.sp021332

Cited by 308 publications

(393 citation statements)

References 42 publications

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“…At least for limb movements this type of SDN is in accord with data from both motoneuronal recording (Matthews 1996) and force production studies (Schmidt et al 1979). We have shown that such SDN arises from a combination of a renewal process at the motoneurons and the recruitment properties of muscles (Jones et al 2002).…”

Section: Discussionsupporting

confidence: 86%

The Main Sequence of Saccades Optimizes Speed-accuracy Trade-off

2006

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In primates, it is well known that there is a consistent relationship between the duration, peak velocity and amplitude of saccadic eye movements, known as the 'main sequence'. The reason why such a stereotyped relationship evolved is unknown. We propose that a fundamental constraint on the deployment of foveal vision lies in the motor system that is perturbed by signaldependent noise on the motor command. This noise imposes a compromise between the speed and accuracy of an eye movement. We propose that saccade trajectories have evolved to optimize a trade-off between the accuracy and duration of the movement. Taking a semi-analytical approach we use Pontryagin's minimum principle to show that there is an optimal trajectory for a given amplitude and duration; and that there is an optimal duration for a given amplitude. It follows that the peak velocity is also fixed for a given amplitude. These predictions are in good agreement with observed saccade trajectories and the main sequence. Moreover, this model predicts a small saccadic dead-zone in which it is better to stay eccentric of target than make a saccade onto target. We conclude that the main sequence has evolved as a strategy to optimize the trade-off between accuracy and speed.

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Section: Discussionsupporting

confidence: 86%

The Main Sequence of Saccades Optimizes Speed-accuracy Trade-off

2006

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“…Here, only results of the case that c(i, j) c for i ± j, i, j 1, ¼, p and c(i, j) c for i ± j, i, j 1, ¼, q for the models without reverse potentials are presented. It has been reported in the literature that the correlation coef®cient between cells is around 0.1 in V5 of rhesus monkeys in vivo (Zohary et al, 1994), and around 0.2 in human motor units of a variety of muscles (Matthews, 1996). Fig.…”

Section: Numerical Resultsmentioning

confidence: 87%

Is the integrate-and-fire model good enough?—a review

Feng

2001

Neural Networks

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We review some recent results on the behaviour of the integrate-and-®re (IF) model, the FitzHugh±Nagumo (FHN) model, a simpli®ed version of the FHN (IF-FHN) model and the Hodgkin±Huxley (HH) model with correlated inputs. The effect of inhibitory inputs on the model behaviour is also taken into account. Here, inputs exclusively take the form of diffusion approximation and correlated inputs mean correlated synaptic inputs (Sections 2 and 3). It is found that the IF and HH models respond to correlated inputs in totally opposite ways, but the IF-FHN model shows similar behaviour to the HH model. Increasing inhibitory input to single neuronal models, such as the FHN model and the HH model can sometimes increase their ®ring rates, which we termed inhibition-boosted ®ring (IBF). Using the IF model and the IF-FHN model, we theoretically explore how and when IBF can happen. The computational complexity of the IF-FHN model is very similar to the conventional IF model, but the former captures some interesting and essential features of biophysical models and could serve as a better model for spiking neuron computation. q

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“…8). However, many studies (Calvin and Stevens, 1968;Granit et al, 1966;Gustafsson and McCrea, 1984;Matthews, 1996) have established that the level of synaptic noise in motoneurons generates a standard deviation of the membrane potential of a few mV. According to our model, at this level of noise (bottom two curves in Fig.…”

Section: Predictions Of the Modelmentioning

confidence: 77%

“…Second, our model allows predictions to be made about the effect of changing the baseline current level. Motoneurons operate over a range of mean input levels, from the subthreshold regime Matthews (1996) to tonic firing (Calvin and Stevens, 1968). If synaptic noise were very low, our model would predict that motoneuron responses to input pulses is very strongly dependent on the mean input level: responsiveness changes dramatically as the mean input approaches threshold and the neuron switches from subthreshold mode to tonic firing (top curve in Fig.…”

Section: Predictions Of the Modelmentioning

confidence: 99%

“…Our diffusive noise model simulates membrane potential fluctuations caused by background synaptic input, the main source of noise in motoneurons: Calvin and Stevens (1968) performed intracellular recordings in cat lumbosacral motoneurons showing that synaptic input causes the membrane potential to fluctuate about an otherwise steady level with an exponentially time-filtered Gaussian distribution. The membrane potential peak-topeak amplitude can reach several mV (Granit et al, 1966;Gustafsson and McCrea, 1984), enough to trigger occasional spikes even when the mean potential is well below threshold; in tonically firing human motoneurons, synaptic noise is known to play a major role (Matthews, 1996). It should be noted that background synaptic activity has also been found to increase conductance significantly in dendrites of neocortical pyramidal neurons and in cerebellar Purkinje cells (Hausser and Clark, 1997), thus decreasing cellular responsiveness; our model does not describe these effects explicitly.…”

Section: Limitations Of the Modelmentioning

confidence: 99%

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Herrmann

Gerstner

2002

Journal of Computational Neuroscience

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Abstract.A generalized version of the integrate-and-fire model is presented that qualitatively reproduces firing rates and membrane trajectories of motoneurons. The description is based on the spike-response model and includes three different time constants: the passive membrane time constant, a recovery time of the input conductance after each spike, and a time constant of the spike afterpotential. The effect of stochastic background input on the peristimulus time histogram (PSTH) response to spike input is calculated analytically. Model results are compared with the experimental data of Poliakov et al. (1996). The linearized theory shows that the PSTH response to an input spike is proportional to a filtered version of the postsynaptic potential generated by the input spike. The shape of the filter depends on the background activity. The full nonlinear theory is in close agreement with simulated PSTH data.

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Relationship of firing intervals of human motor units to the trajectory of post‐spike after‐hyperpolarization and synaptic noise.

Cited by 308 publications

References 42 publications

The Main Sequence of Saccades Optimizes Speed-accuracy Trade-off

The Main Sequence of Saccades Optimizes Speed-accuracy Trade-off

Is the integrate-and-fire model good enough?—a review

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