Computational modeling of channelrhodopsin-2 photocurrent characteristics in relation to neural signaling.

Channelrhodopsins-2 (ChR2) are a class of light sensitive proteins that offer the ability to use light stimulation to regulate neural activity with millisecond precision. In order to address the limitations in the efficacy of the wild-type ChR2 (ChRwt) to achieve this objective, new variants of ChR2 that exhibit fast mon-exponential photocurrent decay characteristics have been recently developed and validated. In this paper, we investigate whether the framework of transition rate model with 4 states, primarily developed to mimic the biexponential photocurrent decay kinetics of ChRwt, as opposed to the low complexity 3 state model, is warranted to mimic the mono-exponential photocurrent decay kinetics of the newly developed fast ChR2 variants: ChETA (Gunaydin et al., Nature Neurosci. 13:387-392, 2010) and ChRET/TC (Berndt et al., Proc. Natl. Acad. Sci. 108:7595-7600, 2011). We begin by estimating the parameters of the 3-state and 4-state models from experimental data on the photocurrent kinetics of ChRwt, ChETA, and ChRET/TC. We then incorporate these models into a fast-spiking interneuron model (Wang and Buzsaki, J. Neurosci. 16:6402-6413, 1996) and a hippocampal pyramidal cell model (Golomb et al., J. Neurophysiol. 96:1912-1926, 2006) and investigate the extent to which the experimentally observed neural response to various optostimulation protocols can be captured by these models. We demonstrate that for all ChR2 variants investigated, the 4 state model implementation is better able to capture neural response consistent with experiments across wide range of optostimulation protocol. We conclude by analytically investigating the conditions under which the characteristic specific to the 3-state model, namely the monoexponential photocurrent decay of the newly developed variants of ChR2, can occur in the framework of the 4-state model.

Computational models of epilepsy.

PURPOSE: Approximately 30% of epilepsy patients suffer from medically refractory epilepsy, in which seizures can not controlled by the use of anti-epileptic drugs (AEDs). Understanding the mechanisms underlying these forms of drug-resistant epileptic seizures and the development of alternative effective treatment strategies are fundamental challenges for modern epilepsy research. In this context, computational modeling has gained prominence as an important tool for tackling the complexity of the epileptic phenomenon. In this review article, we present a survey of computational models of epilepsy from the point of view that epilepsy is a dynamical brain disease that is primarily characterized by unprovoked spontaneous epileptic seizures. METHOD: We introduce key concepts from the mathematical theory of dynamical systems, such as multi-stability and bifurcations, and explain how these concepts aid in our understanding of the brain mechanisms involved in the emergence of epileptic seizures. RESULTS: We present a literature survey of the different computational modeling approaches that are used in the study of epilepsy. Special emphasis is placed on highlighting the fine balance between the degree of model simplification and the extent of biological realism that modelers seek in order to address relevant questions. In this context, we discuss three specific examples from published literature, which exemplify different approaches used for developing computational models of epilepsy. We further explore the potential of recently developed optogenetics tools to provide novel avenue for seizure control. CONCLUSION: We conclude with a discussion on the utility of computational models for the development of new epilepsy treatment protocols.

A computational model of Parkinsonian handwriting that highlights the role of the indirect pathway in the basal ganglia.

Parkinsonian handwriting is typically characterized by micrographia, jagged line contour, and unusual fluctuations in pen velocity. In this paper we present a computational model of handwriting generation that highlights the role of the basal ganglia, particularly the indirect pathway. Whereas reduced dopamine levels resulted in reduced letter size, transition of STN-GPe dynamics from desynchronized (normal) to synchronized (PD) condition resulted in increased fluctuations in velocity in the model. We also present handwriting data from PD patients (n=34) who are at various stages of disease and had taken medication various lengths of time before the handwriting sessions. The patient data are compared with those of age-matched controls. PD handwriting statistically exhibited smaller size and larger velocity fluctuation compared to normal handwriting.