This module is used to inject artificial conductances into a guinea pig cardiomyocytes through dynamic current clamp. An embedded guinea pig ventricular cardiomyocyte model is voltage clamped to the membrane potential of a patch clamped cardiomyocyte. Using one of the calculated currents of the model, an artificial current is scaled and injected into the patch clamped cell. This allows for artificial block or increase of a particular current through dynamic clamp. Currently, the Livshitz Rudy 2009 and Faber Rudy 2000 guinea pig models have been implemented in the module.

Input Channels

1. input(0) – “Input Voltage (V)” : Membrane potential from target cell

Output Channels

1. output(0) – “Output Current (A)” : Current to be applied to the cardiomyocyte

Parameters

1. APD Repolarization % : Repolarization % value used to calculated action potential duration
2. Minimum APD (ms) : The minimum time required for a depolarization to count as an action potential
3. Number of Trials : Number of times the protocol will be run after start protocol button is toggled
4. Interval time : The length of time between protocols
5. BCL (ms): The basic cycle length used during static pacing
6. Stim Mag (nA) : The stimulation magnitude used during all pacing
7. Stim Length (ms) : The length of the stimulation used during all pacing
8. Cm (pF) : Membrane capacitance of the target cell, very important for scaling the model current

The module runs in three different modes. The first mode, toggled through the “Threshold” button, will perform a simple test to estimate the current threshold needed to elicit an action potential. This threshold is then scaled by 1.5 and set to the stimulation magnitude. The second mode is “Static Pacing”. This mode will pace the cell at a static BCL based on the parameters under “Stimulation Parameters”. The third mode is the protocol mode, toggled through the “Start Protocol” button. In this mode, the module will run the protocol set through the protocol editor, which is explained below. Users can also set the parameters used to calculated action potential duration and if the protocol will be run multiple times. The data recorder can be remotely started by checking the “Record Data” checkbox under “Protocol Parameters”. When static pacing or a protocol is started, the data recorder is remotely started. This allows the data recording to be in sync with the beginning and end of a protocol. When multiple trials are being run, the data recorder will start and stop between each trial, automatically appending a new trial to the HDF5 file.

The protocol editor allows the building of a protocol made up of several different steps. These steps can be static pacing or dynamic current clamp. By clicking the “Add Step” button of the protocol editor, a prompt will appear. The type of step is chosen, and the required parameters will need to be inputted. For “Current Scaling” steps, the “Current to Scale” must be inputted with a “Scaling Percentage”. The currents available to scale are – “INa”, “IKr”, “IKs”, “ICaL”, and “IK1″. Note, this parameter is case sensitive. The “Scaling Percentage” is used to increase or decrease the current injected into the cell. A “Scaling Percentage” of 0 will result in no injection (0% change), -50 will result in a 50% decrease, and 25 will result in a 25% increase. During a “Current Scaling” step, the model will be voltage clamped to the membrane potential of the cell through the modules input channel. The scaled current will then be injected into the target cell, which is in turn scaled by the target cell’s membrane capacitance. When the model is active can be controlled through the protocol editor with the “Model: Start”, “Model: Stop”, and “Model: Reset” steps. This allows voltage clamping of the model to start before current scaling is active in order to eliminate transients.

References:
Faber GM, Rudy Y. “Action potential and contractility changes in Na+i overloaded cardiac myocytes: a simulation study”. Biophys J. 2000 May;78(5):2392-404.
Livshitz LM, Rudy Y. “Uniqueness and stability of action potential models during rest, pacing, and conduction using problem-solving environment”. Biophys J. 2009 Sep 2;97(5):1265-76.

This module contains a spike detector based on a positive threshold crossing (see SpikeDetect module). It computes the average ISI and the current coefficient of variation. These values are continuously updated with each spike until the statistics are reset with the button.

Input Channels

1. input(0) – “Vm” : Membrane potential

Output Channels

1. output(0) – “ISI” : Output current (A)

Parameters

1. Threshold (mV) : threshold crossing at which to detect a spike
2. Min Interval (ms) : minimum interval (refractory period) that must pass before another spike is detected

This module sends a single value as a trigger or “kick” to another module. It outputs a user-specified value when triggered and a value of 0 otherwise. It can be used to manually mark events that a user sees in the data. It’s value in the HDF5 file will then serve as an event flag. This module can also be used to manually test other modules that require a trigger (such as those accepting input from the Spike Detector module). This module only sends a single “kick” at the specified delay after it is unpaused.

Input Channels

None

Output Channels

1. output(0) – “Kick” : the kick amplitude of a value of ’0′ otherwise

Parameters

1. Amplitude : the amplitude of the kick
2. Delay (ms) : the delay after the module is unpaused when the kick is given

This module computes coefficients for three types of filters. They require the following parameters:

1. Butterworth: passband edge
2. Chebyshev: passband ripple, passband edge
3. Elliptical: passband ripple, stopband ripple, passband edge, stopband edge

You may save the computed coefficients and the filter’s parameters to a file.

The Butterworth filter is the best compromise between attenuation and phase response. It has no ripple in the pass band or the stop band, and because of this is sometimes called a maximally flat filter. The Butterworth filter achieves its flatness at the expense of a relatively wide transition region from pass band to stop band, with average transient characteristics.

The Chebyshev filter has a smaller transition region than the same-order Butterworth filter, at the expense of ripples in its pass band. The filter minimizes the height of the maximum ripple. If you use a Chebyshev filter, you should also choose the type of normalization to apply.

An Elliptical (Cauer) filter has a shorter transition region than the Chebyshev filter because it allows ripples in both the stop and pass bands, giving a much higher rate of attenuation in the stop band. Elliptical filters give better frequency discrimination, but have a degraded transient response.

Input Channels

1. input(0) – “Input” : Signal to filter

Output Channels

1. output(0) – “Output” : Filtered signal

Parameters

1. Filter Order: an integer for the desired order for the filter
2. Passband Ripple (dB)
3. Passband Edge (Hz)
4. Stopband Ripple (dB)
5. Stopband Edge (Hz)
6. Input quantizing factor: the number of bits to quantize the input signal to
7. Coefficients quantizing factor: the number of bits to quantize the filter coefficients to

This module controls the Intespike interval (ISI) of a neuron using a Proportional Integral controller. The model automatically tunes the PI controller parameters to the neuron. The goal of the PI controller is to make small changes in current at each action potential onset to maintain the neuron at a target firing rate. This spike rate controller is designed to offset drift in the firing rate on the order of 10s of seconds to minutes. This module requires input from the “Spike Detector” module.

Input Channels

1. input(0) – “State” : Spike Detector output

Output Channels

1. output(0) – “Iout” : Current to be applied to the neuron. Connect this output to the neuron. (pA)
2. output(1) – “Target ISI” : desired ISI (s)
3. output(2) – “ISI” : actual ISI (s)

Parameters

1. Threshold (mV) : threshold crossing at which to detect a spike
2. Min Interval (ms) : minimum interval (refractory period) that must pass before another spike is detected
3. Kp : Proportional Gain
4. Ti : Integral Time
5. Td : Derivative Time
6. Target Ts (s): Target Inter Spike Interval
7. Constant (pA): Current, Constant Current
8. Increase : % of current increase per step
9. Autotune

The module automatically calculates a first order model for the ISI as a function of the applied current. Given the first order model, it solves the coefficients for the critically damped solution of the proportional-integral controller with the constraint that the proportional coefficient is 1/100 of the Integral coefficient, to assure small changes with each spike,.
Once a cell is patched, the spikes are detected with the spike detector module. The spike detector is connected to the input of the Auto-PI module and the Auto-PI’s output is sent back to the neuron. Set the desired ISI, set AUTOTUNE = 1 and unpause the module. The module will automatically apply a set of current steps to find an applied current to make neuron fire near the target firing rate, then it perturbs the current measuring the neuron’s response in time with each change. Once a first order model is fit to the neuron’s response, the AUTOTUNE turns off, setting the coefficients and switches automatically to closed loop control. Within about 10 spikes, the neuron should be close to the target firing rate.

Reference: Miranda-Domínguez O, Gonia J, Netoff TI. Firing rate control of a neuron using a linear proportional-integral controller. J Neural Eng. 2010 Dec;7(6):066004.

This module implements a protocol for performing conduction block experiments on nerve fibers using high frequency AC current (HFAC) stimuli. Action potentials in the nerve are evoked with a single biphase square pulse and the HFAC signal is a sinusoidal waveform. You have an option to select which component of the biphase pulse occurs first. The “Single AP Stim” button is used to send a single pulse that can be used to elicit individual responses. The “Enable HFAC” toggle button can be used at any time to turn the HFAC signal on or off. The “Run” button starts a protocol in which the HFAC signal is enabled and the action potential stimulus is automatically triggered after a certain delay. Synchronizing this module with the Data Recorder will create separate trials in the HDF5 file corresponding to the protocols initiated with the “Run” button.

Input Channels

None

Output Channels

1. output(0) – “AP Stim” : action potential stimulus signal (biphasic square wave)
2. output(1) – “HFAC Signal” : sinusoidal high frequency AC signal

Parameters

1. AP Stim Amplitude : Amplitude of the action potential stimulus
2. AP Stim Delay (ms) : Delay after which to trigger the AP stimulus during a timed protocol
3. AP Stim Width (ms) : The width of each component of the biphasic AP stimulus
4. HFAC Freq (kHz) : Frequency of the HFAC signal
5. HFAC Amplitude : Amplitude of the HFAC signal
6. Trial Duration : the total duration of a single trial, the Data Recorder will keep recording for this duration when running a timed protocol
7. Data File Name : file name to use for the Data Recorder

Computational studies as well as in vivo and in vitro results have shown that many cortical neurons fire in a highly irregular manner and at low average firing rates. These patterns seem to persist even when highly rhythmic signals are recorded by local field potential electrodes or other methods that quantify the summed behavior of a local population. Models of the 30-80 Hz gamma rhythm in which network oscillations arise through ‘stochastic synchrony’ capture the variability observed in the spike output of single cells while preserving network-level organization. We extend upon these results by constructing model networks constrained by experimental measurements and using them to probe the effect of biophysical parameters on network-level activity. We find in simulations that gamma-frequency oscillations are enabled by a high level of incoherent synaptic conductance input, similar to the barrage of noisy synaptic input that cortical neurons have been shown to receive in vivo. This incoherent synaptic input increases the emergent network frequency by shortening the time scale of the membrane in excitatory neurons and by reducing the temporal separation between excitation and inhibition due to decreased spike latency in inhibitory neurons. These mechanisms are demonstrated in simulations and in vitro current-clamp and dynamic-clamp experiments. Simulation results further indicate that the membrane potential noise amplitude has a large impact on network frequency and that the balance between excitatory and inhibitory currents controls network stability and sensitivity to external inputs.

How stable synchrony in neuronal networks is sustained in the presence of conduction delays is an open question. The Dynamic Clamp was used to measure phase resetting curves (PRCs) for entorhinal cortical cells, and then to construct networks of two such neurons. PRCs were in general Type I (all advances or all delays) or weakly type II with a small region at early phases with the opposite type of resetting. We used previously developed theoretical methods based on PRCs under the assumption of pulsatile coupling to predict the delays that synchronize these hybrid circuits. For excitatory coupling, synchrony was predicted and observed only with no delay and for delays greater than half a network period that cause each neuron to receive an input late in its firing cycle and almost immediately fire an action potential. Synchronization for these long delays was surprisingly tight and robust to the noise and heterogeneity inherent in a biological system. In contrast to excitatory coupling, inhibitory coupling led to antiphase for no delay, very short delays and delays close to a network period, but to near-synchrony for a wide range of relatively short delays. PRC-based methods show that conduction delays can stabilize synchrony in several ways, including neutralizing a discontinuity introduced by strong inhibition, favoring synchrony in the case of noisy bistability, and avoiding an initial destabilizing region of a weakly type II PRC. PRCs can identify optimal conduction delays favoring synchronization at a given frequency, and also predict robustness to noise and heterogeneity.

The variations of the intracellular concentration of calcium ion ([Ca(2+)](i)) are at the heart of intracellular signaling, and their imaging is therefore of enormous interest. However, passive [Ca(2+)](i) imaging provides no control over these variations, meaning that a full exploration of the functional consequences of [Ca(2+)](i) changes is difficult to attain. The tools designed so far to modify [Ca(2+)](i), even qualitatively, suffer drawbacks that undermine their widespread use. Here, we describe an electro-optical technique to quantitatively set [Ca(2+)](i), in real time and with sub-cellular resolution, using two-photon Ca(2+) uncaging and dynamic-clamp. We experimentally demonstrate, on neurons from acute olfactory bulb slices of Long Evans rats, various capabilities of this technique previously difficult to achieve, such as the independent control of the membrane potential and [Ca(2+)](i) variations, the functional knocking-in of user-defined virtual voltage-dependent Ca(2+) channels, and the standardization of [Ca(2+)](i) patterns across different cells. Our goal is to lay the groundwork for this technique and establish it as a new and versatile tool for the study of cell signaling.

Sudden cardiac death (SCD) due to ventricular fibrillation (VF) is a major world-wide health problem. Common triggers of VF are abnormal repolarizations of the cardiac action potential, known as early afterdepolarizations (EADs). Here we used a hybrid biological-computational approach to investigate the dependence of EADs on the biophysical properties of the L-type Ca(2+) current (I(Ca,L)) and to explore how modifications of these properties could be designed to suppress EADs. EADs were induced in isolated rabbit ventricular myocytes by exposure to 600 ?mol/L H(2)O(2) (oxidative stress) or lowering the external [K(+)] from 5.4 to 2.0-2.7 mmol/L (hypokalemia). The role of I(Ca,L) in EAD formation was directly assessed using the dynamic clamp technique: the paced myocyte’s Vm was input to a myocyte model with tunable biophysical parameters, which computed a virtual I(Ca,L), that was injected into the myocyte in real time, replacing the endogenous I(Ca,L) which was suppressed with nifedipine. Injecting a current with the native I(Ca,L) biophysical properties restored EAD occurrence in myocytes challenged by H(2)O(2) or hypokalemia. A mere 5 mV depolarizing shift in the voltage dependence of activation or a hyperpolarizing shift in the steady-state inactivation curve completely abolished EADs in myocytes while maintaining a normal Ca(i) transient. We propose that modifying I(Ca,L) biophysical properties has potential as a powerful therapeutic strategy for suppressing EADs and EAD-mediated arrhythmias.