Untitled

Herrmann, Alix; Gerstner, Wulfram

doi:10.1023/a:1015739523224

Cited by 18 publications

(5 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Motoneuron. For simulating motoneuron activation and rate coding, we utilized a deterministic model equivalent to the integrate-andfire model for motoneurons as reported in Herrmann and Gerstner (2002). In this model, between successive spikes, the membrane potential is…”

Section: Methodsmentioning

confidence: 99%

Simulation of electromyographic recordings following transcranial magnetic stimulation

Moezzi

Schaworonkow

Plogmacher

et al. 2018

Journal of Neurophysiology

View full text Add to dashboard Cite

Transcranial magnetic stimulation (TMS) is a technique that enables noninvasive manipulation of neural activity and holds promise in both clinical and basic research settings. The effect of TMS on the motor cortex is often measured by electromyography (EMG) recordings from a small hand muscle. However, the details of how TMS generates responses measured with EMG are not completely understood. We aim to develop a biophysically detailed computational model to study the potential mechanisms underlying the generation of EMG signals following TMS. Our model comprises a feed-forward network of cortical layer 2/3 cells, which drive morphologically detailed layer 5 corticomotoneuronal cells, which in turn project to a pool of motoneurons. EMG signals are modeled as the sum of motor unit action potentials. EMG recordings from the first dorsal interosseous (FDI) muscle were performed in four subjects and compared to simulated EMG signals. Our model successfully reproduces several characteristics of the experimental data. The simulated EMG signals match experimental EMG recordings in shape and size, and change with stimulus intensity and contraction level as in experimental recordings. They exhibit cortical silent periods that are close to the biological values, and reveal an interesting dependence on inhibitory synaptic transmission properties. Our model predicts several characteristics of the firing patterns of neurons along the entire pathway from cortical layer 2/3 cells down to spinal motoneurons and should be considered as a viable tool for explaining and analyzing EMG signals following TMS.

show abstract

Section: Methodsmentioning

confidence: 99%

Simulation of electromyographic recordings following transcranial magnetic stimulation

Moezzi

Schaworonkow

Plogmacher

et al. 2018

Journal of Neurophysiology

View full text Add to dashboard Cite

show abstract

“…Although qualitatively similar to the observed PSTH, these simple trans-formations of the PSP never yielded an accurate quantitative prediction of spike rate changes after a stimulus, especially in the case of strong inhibition (Fetz and Gustafsson 1983). General nonlinear system identification methods produced more accurate predictions but did not have a direct biophysical interpretation (Herrmann and Gerstner 2002;Mease et al 2014;Poliakov et al 1997).…”

Section: Introductionmentioning

confidence: 93%

Predicting responses to inhibitory synaptic input in substantia nigra pars reticulata neurons

Simmons

Higgs

Lebby

et al. 2018

Journal of Neurophysiology

View full text Add to dashboard Cite

The changes in firing probability produced by a synaptic input are usually visualized using the post-stimulus time histogram (PSTH). It would be useful if postsynaptic firing patterns could be predicted from patterns of afferent synaptic activation, but attempts to predict the PSTH from synaptic potential waveforms using reasoning based on voltage trajectory and spike threshold have not been successful, especially for inhibitory inputs. We measured PSTHs for substantia nigra pars reticulata (SNr) neurons inhibited by optogenetic stimulation of striato-nigral inputs, or by matching artificial inhibitory conductances applied by dynamic clamp. The PSTH was predicted by a model based on each SNr cell's phase resetting curve (PRC). Optogenetic activation of striato-nigral input or artificial synaptic inhibition produced a PSTH consisting of an initial depression of firing followed by oscillatory increases and decreases repeating at the SNr cell's baseline firing rate. The phase resetting model produced PSTHs closely resembling the cell data, including the primary pause in firing and the oscillation. Key features of the PSTH, including the onset rate and duration of the initial inhibitory phase, and the subsequent increase in firing probability could be explained from the characteristic shape of the SNr cell's PRC. The rate of damping of the late oscillation was explained by the influence of asynchronous phase perturbations producing firing rate jitter and wander. Our results demonstrate the utility of phase resetting models as a general method for predicting firing in spontaneously active neurons, and their value in interpretation of the striato-nigral PSTH.

show abstract

“…The membrane potential of the motoneuron is given by an LIF model (Eq. 3 ) equivalent to that of Herrmann and Gerstner (2002) ( Herrmann and Gerstner, 2002 ; Moezzi et al, 2018 ):

where

is the last spike timing of the j th motoneuron. h(t) is the Heaviside step function, which is 0 when its argument is negative and 1 otherwise.…”

Section: Methodsmentioning

confidence: 99%

Model-based closed-loop control of thalamic deep brain stimulation

Tian,

Saradhi,

Bello

et al. 2024

Front. Netw. Physiol.

View full text Add to dashboard Cite

Introduction: Closed-loop control of deep brain stimulation (DBS) is beneficial for effective and automatic treatment of various neurological disorders like Parkinson’s disease (PD) and essential tremor (ET). Manual (open-loop) DBS programming solely based on clinical observations relies on neurologists’ expertise and patients’ experience. Continuous stimulation in open-loop DBS may decrease battery life and cause side effects. On the contrary, a closed-loop DBS system uses a feedback biomarker/signal to track worsening (or improving) of patients’ symptoms and offers several advantages compared to the open-loop DBS system. Existing closed-loop DBS control systems do not incorporate physiological mechanisms underlying DBS or symptoms, e.g., how DBS modulates dynamics of synaptic plasticity.Methods: In this work, we propose a computational framework for development of a model-based DBS controller where a neural model can describe the relationship between DBS and neural activity and a polynomial-based approximation can estimate the relationship between neural and behavioral activities. A controller is used in our model in a quasi-real-time manner to find DBS patterns that significantly reduce the worsening of symptoms. By using the proposed computational framework, these DBS patterns can be tested clinically by predicting the effect of DBS before delivering it to the patient. We applied this framework to the problem of finding optimal DBS frequencies for essential tremor given electromyography (EMG) recordings solely. Building on our recent network model of ventral intermediate nuclei (Vim), the main surgical target of the tremor, in response to DBS, we developed neural model simulation in which physiological mechanisms underlying Vim–DBS are linked to symptomatic changes in EMG signals. By using a proportional–integral–derivative (PID) controller, we showed that a closed-loop system can track EMG signals and adjust the stimulation frequency of Vim–DBS so that the power of EMG reaches a desired control target.Results and discussion: We demonstrated that the model-based DBS frequency aligns well with that used in clinical studies. Our model-based closed-loop system is adaptable to different control targets and can potentially be used for different diseases and personalized systems.

show abstract

Untitled

Cited by 18 publications

References 32 publications

Simulation of electromyographic recordings following transcranial magnetic stimulation

Simulation of electromyographic recordings following transcranial magnetic stimulation

Predicting responses to inhibitory synaptic input in substantia nigra pars reticulata neurons

Model-based closed-loop control of thalamic deep brain stimulation

Contact Info

Product

Resources

About