Abstract

The development of biologically realistic models of brain microcircuits and regions is currently a very relevant topic in computational neuroscience. From basic research to clinical applications, there is an increasing demand for accurate models that incorporate local cellular and network specificities, able to capture a broad range of dynamics and functions associated with given brain regions. One of the main challenges of these models is the passage between different scales, going from the microscale (cellular) to the meso (microcircuit) and macroscale (region or whole-brain level), while keeping at the same time a constraint on the demand of computational resources. One novel approach to this problem is the use of mean-field models of neuronal activity to build large-scale simulations. This provides an effective solution to the passage between scales with relatively low computational demands, which is achieved by a drastic reduction in the dimensionality of the system. In this paper we introduce a multiscale modelling framework for the hippocampal CA1, a region of the brain that plays a key role in functions such as learning, memory consolidation and navigation. Our modelling framework goes from the single cell level to the macroscale and makes use of a novel mean-field model of CA1, introduced in this paper, to bridge the gap between the micro and macro scales. To develop the mean-field model we make use of a recently introduced formalism based on a bottom-up approach that is easily applicable to different neuronal models and cell types. We test and validate the model by analyzing the response of the system to the main brain rhythms observed in the hippocampus and comparing our results with the ones of the corresponding spiking network model of CA1. In addition, we show an example of the implementation of our model to study a stimulus propagation at the macro-scale, and we compare the results obtained from our model with the corresponding spiking network model of the whole CA1 area.