Abstract
Neuron behavior results from the interplay between networks of biochemical processes and electrical signaling. Synaptic plasticity is one of the neuronal properties emerging from such an interaction. One of the current approaches to study plasticity is to model either its electrical aspects or its biochemical components. Among the chief reasons are the different time scales involved, electrical events happening in milliseconds while biochemical cascades respond in minutes or hours. In order to create multiscale models taking in consideration both aspects simultaneously, one needs to synchronize the two models, and exchange relevant variable values. We present a new event-driven algorithm to synchronize different neuronal models, which decreases computational time and avoids superfluous synchronizations. The algorithm is implemented in the TimeScales framework. We demonstrate its use by simulating a new multiscale model of the Medium Spiny Neuron of the Neostriatum. The model comprises over a thousand dendritic spines, where the electrical model interacts with the respective instances of a biochemical model. Our results show that a multiscale model is able to exhibit changes of synaptic plasticity as a result of the interaction between electrical and biochemical signaling. Our synchronization strategy is general enough to be used in simulations of other models with similar synchronization issues, such as networks of neurons. Moreover, the integration between the electrical and the biochemical models opens up the possibility to investigate multiscale process, like synaptic plasticity, in a more global manner, while taking into account a more realistic description of the underlying mechanisms.
Highlights
A model can be considered multiscale when at least one of these conditions is true: (i) the variables used in the model span different orders of magnitude either in time or space, e.g. from milliseconds to minutes, or from nanometers to millimeters; (ii) parts of the model are simulated with different time and/or spatial resolutions, influencing each other through a systematic exchange of variables [1]
In the simulation we presented, two main questions have been addressed: (i) how different frequencies affect the response of a spine when the biochemical contribution is taken in consideration, (ii) how a stimulated spine influences neighboring spines
While it is known that the input frequency and the duration have an effect on the depolarization of the spine [24], the role of the biochemical component is usually not investigated due to the longer timescales
Summary
A model can be considered multiscale when at least one of these conditions is true: (i) the variables used in the model span different orders of magnitude either in time or space, e.g. from milliseconds to minutes, or from nanometers to millimeters; (ii) parts of the model are simulated with different time and/or spatial resolutions, influencing each other through a systematic exchange of variables [1]. While spatial stochastic simulations of a femtoliter and a few thousands molecules are computationally tractable and can provide good approximations of the dynamics happening within a spine of a neuron [2], they are hardly usable for an entire neuron Neither are they desirable if we only need to understand the electrical properties of the cell. A possible solution could be to integrate spatial simulation of small compartments with multicompartment Hodgkin-Huxley based models These multiscale models could help understand the overall electrical behavior of the neuron, keeping the ability of zooming in a specific part of the neuron, using the spatial simulations to gain detailed understanding of the biochemical reactions occurring there. A possible solution is to model large networks using integrate and fire neurons [5,6], and integrate them with multicompartment models only in the cases where a detailed understanding of electrophysiology at subcellular level is needed
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