Double Two-State Opsin Model With Autonomous Parameter Inference

Abstract

Optogenetics has a lot of potential to become an effective neuromodulative therapy for clinical applications. Selecting the correct opsin is crucial to have an optimal optogenetic tool. With computational modeling, the neuronal response to the current dynamics of an opsin can be extensively and systematically tested. Unlike electrical stimulation where the effect is directly defined by the applied field, the stimulation in optogenetics is indirect, depending on the selected opsin's non-linear kinetics. With the continuous expansion of opsin possibilities, computational studies are difficult due to the need for an accurate model of the selected opsin first. To this end, we propose a double two-state opsin model as alternative to the conventional three and four state Markov models used for opsin modeling. Furthermore, we provide a fitting procedure, which allows for autonomous model fitting starting from a vast parameter space. With this procedure, we successfully fitted two distinctive opsins (ChR2(H134R) and MerMAID). Both models are able to represent the experimental data with great accuracy and were obtained within an acceptable time frame. This is due to the absence of differential equations in the fitting procedure, with an enormous reduction in computational cost as result. The performance of the proposed model with a fit to ChR2(H134R) was tested, by comparing the neural response in a regular spiking neuron to the response obtained with the non-instantaneous, four state Markov model (4SB), derived by Williams et al. (2013). Finally, a computational speed gain was observed with the proposed model in a regular spiking and sparse Pyramidal-Interneuron-Network-Gamma (sPING) network simulation with respect to the 4SB-model, due to the former having two differential equations less. Consequently, the proposed model allows for computationally efficient optogenetic neurostimulation and with the proposed fitting procedure will be valuable for further research in the field of optogenetics.

Removing direct photocurrent artifacts in optogenetic connectivity mapping data via constrained matrix factorization

“…Photocurrent waveforms were simulated using the model from ref. [ 16 ], with parameters chosen at random to capture a wide variety of photocurrent kinetics. Our simulated mapping experiments include a great deal of biological detail not directly present in the photocurrent removal model, including power-dependent PSC latency and spontaneous PSCs.…”

“…This captures scenarios in which photocurrents and PSCs from the prior trial may not have decayed to zero before the next stimulus. Simulated photocurrents used a variant of the double two-state opsin model described by [ 16 ], represented by the following set of equations: where Θ ( t ) is the heaviside step function, and I photo is the induced photocurrent. The parameters governing the shape of the photocurrent waveform, R inf , O inf , τ o , τ r , were selected uniformly at random from a range of values to match variability in photocurrent kinetics in the real data (see Fig 2 ).…”

Optogenetic stimulation recruits cortical neurons in a morphology-dependent manner

“…Various computational research investigated optogenetic stimulation of single neurons (18, 19, 33–35). However, to the best of our knowledge, only (18) and (19) have studied the spatial dependence of optogenetic stimulation on the relative position of the light source to the neuron. Specifically, these studies focused on characterizing the stimulation intensity threshold required to induce neuronal spiking.…”

“…The role of morphology for optogenetic responses has primarily been approached through computational modeling, mitigating the experimental challenge of monitoring intracellular activity of a single neuron under many stimulation conditions. Previous work shed light on the excitability of neurons along their morphology (15, 16) and revealed how the volume of excited neurons is affected by neuronal and stimulation parameters (17). Yet, the level of spatial precision of optogenetic stimulation across varying illumination conditions and in different types of neurons remains unclear.…”

Quantitative analysis of the optogenetic excitability of CA1 neurons

“…In 2006, a first mathematical description of the channel dynamics of ChR2 was published (Nikolic et al, 2006 ). Since then, several studies have been published that use this and other, more accurate and efficient models in combination with neuron models to design in silico experiments (Nikolic et al, 2009 ; Grossman et al, 2011 ; Schneider et al, 2013 ; Williams et al, 2013 ; Gupta et al, 2019 ; Bansal et al, 2021 ; Schoeters et al, 2021 ). These experiments allow for easy parameter manipulation and exploration of the stimulation parameter space.…”